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Item Transcriptional Dynamics During Rhodococcus erythropolis Infection with Phage WC1(Springer Nature, 2024-04-01) Willner, Dana; Paudel, Sudip; Halleran, Andrew D.; Solini, Grace E.; Gray, Veronica; Saha, MargaretBackground Belonging to the Actinobacteria phylum, members of the Rhodococcus genus thrive in soil, water, and even intracellularly. While most species are non-pathogenic, several cause respiratory disease in animals and, more rarely, in humans. Over 100 phages that infect Rhodococcus species have been isolated but despite their importance for Rhodococcus ecology and biotechnology applications, little is known regarding the molecular genetic interactions between phage and host during infection. To address this need, we report RNA-Seq analysis of a novel Rhodococcus erythopolis phage, WC1, analyzing both the phage and host transcriptome at various stages throughout the infection process. Results By five minutes post-infection WC1 showed upregulation of a CAS-4 family exonuclease, putative immunity repressor, an anti-restriction protein, while the host showed strong upregulation of DNA replication, SOS repair, and ribosomal protein genes. By 30 min post-infection, WC1 DNA synthesis genes were strongly upregulated while the host showed increased expression of transcriptional and translational machinery and downregulation of genes involved in carbon, energy, and lipid metabolism pathways. By 60 min WC1 strongly upregulated structural genes while the host showed a dramatic disruption of metal ion homeostasis. There was significant expression of both host and phage non-coding genes at all time points. While host gene expression declined over the course of infection, our results indicate that phage may exert more selective control, preserving the host’s regulatory mechanisms to create an environment conducive for virion production. Conclusions The Rhodococcus genus is well recognized for its ability to synthesize valuable compounds, particularly steroids, as well as its capacity to degrade a wide range of harmful environmental pollutants. A detailed understanding of these phage-host interactions and gene expression is not only essential for understanding the ecology of this important genus, but will also facilitate development of phage-mediated strategies for bioremediation as well as biocontrol in industrial processes and biomedical applications. Given the current lack of detailed global gene expression studies on any Rhodococcus species, our study addresses a pressing need to identify tools and genes, such as F6 and rpf, that can enhance the capacity of Rhodococcus species for bioremediation, biosynthesis and pathogen control.Item Limitations of 16S rRNA Gene Sequencing to Characterize Lactobacillus Species in the Upper Genital Tract(Frontiers, 2021-07-01) O'Callaghan, Jessica L.; Willner, Dana; Buttini, Melissa; Huygens, Flavia; Pelzer, Elise S.The endometrial cavity is an upper genital tract site previously thought as sterile, however, advances in culture-independent, next-generation sequencing technology have revealed that this low-biomass site harbors a rich microbial community which includes multiple Lactobacillus species. These bacteria are considered to be the most abundant non-pathogenic genital tract commensals. Next-generation sequencing of the female lower genital tract has revealed significant variation amongst microbial community composition with respect to Lactobacillus sp. in samples collected from healthy women and women with urogenital conditions. The aim of this study was to evaluate our ability to characterize members of the genital tract microbial community to species-level taxonomy using variable regions of the 16S rRNA gene. Samples were interrogated for the presence of microbial DNA using next-generation sequencing technology that targets the V5–V8 regions of the 16S rRNA gene and compared to speciation using qPCR. We also performed re-analysis of published data using alternate variable regions of the 16S rRNA gene. In this analysis, we explore next-generation sequencing of clinical genital tract isolates as a method for high throughput identification to species-level of key Lactobacillus sp. Data revealed that characterization of genital tract taxa is hindered by a lack of a consensus protocol and 16S rRNA gene region target allowing comparison between studies.Item A Language Framework for Modeling Social Media Account Behavior(Springer, 2023-08-01) Nwala, Alexander C.; Flammini, Alessandro; Menczer, FilippoMalicious actors exploit social media to inflate stock prices, sway elections, spread misinformation, and sow discord. To these ends, they employ tactics that include the use of inauthentic accounts and campaigns. Methods to detect these abuses currently rely on features specifically designed to target suspicious behaviors. However, the effectiveness of these methods decays as malicious behaviors evolve. To address this challenge, we propose a language framework for modeling social media account behaviors. Words in this framework, called BLOC, consist of symbols drawn from distinct alphabets representing user actions and content. Languages from the framework are highly flexible and can be applied to model a broad spectrum of legitimate and suspicious online behaviors without extensive fine-tuning. Using BLOC to represent the behaviors of Twitter accounts, we achieve performance comparable to or better than state-of-the-art methods in the detection of social bots and coordinated inauthentic behavior.Item Crowdsourcing Street View Imagery: A Comparison of Mapillary and OpenStreetCam(MDPI, 2020-05-01) Mahabir, Ron; Schuchard, Ross; Crooks, Andrew; Croitoru, Arie; Stefanidis, AnthonyOver the last decade, Volunteered Geographic Information (VGI) has emerged as a viable source of information on cities. During this time, the nature of VGI has been evolving, with new types and sources of data continually being added. In light of this trend, this paper explores one such type of VGI data: Volunteered Street View Imagery (VSVI). Two VSVI sources, Mapillary and OpenStreetCam, were extracted and analyzed to study road coverage and contribution patterns for four US metropolitan areas. Results show that coverage patterns vary across sites, with most contributions occurring along local roads and in populated areas. We also found that a few users contributed most of the data. Moreover, the results suggest that most data are being collected during three distinct times of day (i.e., morning, lunch and late afternoon). The paper concludes with a discussion that while VSVI data is still relatively new, it has the potential to be a rich source of spatial and temporal information for monitoring citiesItem Predicting Micronutrient Deficiency with Publicly Available Satellite Data(Wiley, 2023-03-01) Bondi-Kelly, Elizabeth; Chen, Haipeng; Golden, Christopher D.; Behari, Nikhil; Tambe, MilindMicronutrient deficiency (MND), which is a form of malnutrition that can have serious health consequences, is difficult to diagnose in early stages without blood draws, which are expensive and time-consuming to collect and process. It is even more difficult at a public health scale seeking to identify regions at higher risk of MND. To provide data more widely and frequently, we propose an accurate, scalable, low-cost, and interpretable regional-level MND prediction system. Specifically, our work is the first to use satellite data, such as forest cover, weather, and presence of water, to predict deficiency of micronutrients such as iron, Vitamin B12, and Vitamin A, directly from their biomarkers. We use real-world, ground truth biomarker data collected from four different regions across Madagascar for training, and demonstrate that satellite data are viable for predicting regional-level MND, surprisingly exceeding the performance of baseline predictions based only on survey responses. Our method could be broadly applied to other countries where satellite data are available, and potentially create high societal impact if these predictions are used by policy makers, public health officials, or healthcare providers.Item 'Flux+Mutability': A Conditional Generative Approach to One-class Classification and Anomaly Detection(IOP Publishing, 2022-11-01) Fanelli, Cristiano; Giroux, James; Papandreou, Z.Anomaly Detection is becoming increasingly popular within the experimental physics community. At experiments such as the Large Hadron Collider, anomaly detection is growing in interest for finding new physics beyond the Standard Model. This paper details the implementation of a novel Machine Learning architecture, called Flux+Mutability, which combines cutting-edge conditional generative models with clustering algorithms. In the 'flux' stage we learn the distribution of a reference class. The 'mutability' stage at inference addresses if data significantly deviates from the reference class. We demonstrate the validity of our approach and its connection to multiple problems spanning from one-class classification to anomaly detection. In particular, we apply our method to the isolation of neutral showers in an electromagnetic calorimeter and show its performance in detecting anomalous dijets events from standard QCD background. This approach limits assumptions on the reference sample and remains agnostic to the complementary class of objects of a given problem. We describe the possibility of dynamically generating a reference population and defining selection criteria via quantile cuts. Remarkably this flexible architecture can be deployed for a wide range of problems, and applications like multi-class classification or data quality control are left for further exploration.Item Artificial Intelligence for the Electron Ion Collider (AI4EIC)(Springer, 2024-02-01) Allaire, C.; Fanelli, Cristiano; Giroux, James; Niestroy, Joey; Stevens, Justin R.; Stone, Patrick; Suarez, L.; Suresh, K.; Walter, EricThe Electron-Ion Collider (EIC), a state-of-the-art facility for studying the strong force, is expected to begin commissioning its first experiments in 2028. This is an opportune time for artificial intelligence (AI) to be included from the start at this facility and in all phases that lead up to the experiments. The second annual workshop organized by the AI4EIC working group, which recently took place, centered on exploring all current and prospective application areas of AI for the EIC. This workshop is not only beneficial for the EIC, but also provides valuable insights for the newly established ePIC collaboration at EIC. This paper summarizes the different activities and R&D projects covered across the sessions of the workshop and provides an overview of the goals, approaches and strategies regarding AI/ML in the EIC community, as well as cutting-edge techniques currently studied in other experiments.Item ELUQuant: Event-level Uncertainty Quantification in Deep Inelastic Scattering(IOP Publishing, 2024-01-01) Fanelli, Cristiano; Giroux, JamesWe introduce a physics-informed Bayesian neural network with flow-approximated posteriors using multiplicative normalizing flows for detailed uncertainty quantification (UQ) at the physics event-level. Our method is capable of identifying both heteroskedastic aleatoric and epistemic uncertainties, providing granular physical insights. Applied to deep inelastic scattering (DIS) events, our model effectively extracts the kinematic variables x, Q2, and y, matching the performance of recent deep learning regression techniques but with the critical enhancement of event-level UQ. This detailed description of the underlying uncertainty proves invaluable for decision-making, especially in tasks like event filtering. It also allows for the reduction of true inaccuracies without directly accessing the ground truth. A thorough DIS simulation using the H1 detector at HERA indicates possible applications for the future electron–ion collider. Additionally, this paves the way for related tasks such as data quality monitoring and anomaly detection. Remarkably, our approach effectively processes large samples at high rates.Item Postpandemic Outlook for Organized Criminal Activities: Agility Across the Physical, Social, and Cyber Spaces(National Intelligence University Press, 2022-01-01) Jones, Jim; Stefanidis, AnthonyThe global COVID-19 pandemic and response affected every aspect of our society, including the activities of criminal organizations. In this chapter, we discuss several examples of criminal organization agility during the pandemic, drawn from the physical, social, and cyber domains. We assess that criminal organizations are emerging from the pandemic stronger than before, the pandemic presents a unique opportunity to study criminal organization agility, and criminal organizations are more exposed after their pandemic-driven adjustments. We also assess that this adjusted criminal activity and other factors, including risky operations that expose discoverable data, create investigative opportunities that will enable a deeper understanding of criminal organization structure and will enhance our ability to disrupt and dismantle the organizations behind a broad range of illegal activity.