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An increasingly apparent role of noncoding RNA (ncRNAs) is to coordinate gene expression during environmental stress. A mounting body of evidence implicates small RNAs (sRNAs) as key drivers of Salmonella stress survival. Generally thought to be 50–500 nucleotides in length and to occur in intergenic regions, sRNAs typically regulate protein expression through base pairing with mRNA targets. In this work, through employing a refined definition of sRNAs allowing for shorter sequences and sRNA loci to overlap with annotated protein-coding gene loci, we have identified 475 previously unannotated sRNAs that are significantly differentially expressed during carbon starvation (C-starvation). Northern blotting and quantitative RT-PCRs confirm the expressions and identities of several of these novel sRNAs, and our computational analyses find the majority to be highly conserved and structurally related to known sRNAs. Importantly, we show that deletion of one of the sRNAs dynamically expressed during C-starvation, sRNA4130247, significantly impairs the Salmonella C-starvation response (CSR), confirming its involvement in the Salmonella CSR. In conclusion, the work presented here provides the first-ever characterization of intragenic sRNAs in Salmonella, experimentally confirms that sRNAs dynamically expressed during the CSR are directly involved in stress survival, and more than doubles the Salmonella enterica sRNAs described to date. 

Developments in virtual containers, especially in the cloud infrastructure, have led to diversification of jobs that containers are being used to support, particularly in the big data and machine learning spaces. The diversification has been powered by the adoption of orchestration systems that marshal fleets of containers to accomplish complex programming tasks. The additional components in the vertical technology stack, plus the continued horizontal scaling have led to questions regarding how to forensically analyze complicated technology stacks. This paper proposed a solution through the use of introspection. An exploratory case study has been conducted on a bare-metal cloud that utilizes Kubernetes, the introspection tool Prometheus, and Apache Spark. The contribution of this research is two-fold. First, it provides empirical support that introspection tools can acquire forensically viable data from different levels of a technology stack. Second, it provides the ground work for comparisons between different virtual container platforms. 

Distributed file systems present distinctive forensic challenges in comparison to traditional locally mounted file system volume. Storage device media can number in the thousands, and forensic investigations in this setting necessitate a tailored approach to data collection. The Hadoop Distributed File System (HFDS) produces and maintains partially persistent metadata that is pursuant with a logical volume, a file system, and file addresses on the centralized server. Hence, this research investigates the viability of using a residual central server digital artifact to generate a history model of the distributed file system. The history model affords an investigator a high-level perspective of low-level events to narrow investigative process obligations. The model is generated through set-theoretic relations of the file system essential data structure. Graph-theoretic ordering is applied to the events to provide a history model. The research contribution is a rapid reconstruction of the HDFS storage state transitions generating timelines for system events to forensically assess HDFS properties with conceptual similarity to traditional low-level file system forensic tool output. The results of this research provide a prototype tool, DFS3, for rapid and noninvasive data storage state timeline reconstruction in a big data distributed file system. 

Large-scale adoption of virtual containers has stimulated concerns by practitioners and academics about the viability of data acquisition and reliability due to the decreasing window to gather relevant data points. These concerns prompted the idea that introspection tools, which are able to acquire data from a system as it is running, can be utilized as both an early warning system to protect that system and as a data capture system that collects data that would be valuable from a digital forensic perspective. An exploratory case study was conducted utilizing a Docker engine and Prometheus as the introspection tool. The research contribution of this research is two-fold. First, it provides empirical support for the idea that introspection tools can be utilized to ascertain differences between pristine and infected containers. Second, it provides the ground work for future research conducting an analysis of large-scale containerized applications in a virtual cloud.