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Background Radiation therapy is integral to effective thoracic cancer treatments, but its application is limited by sensitivity of critical organs such as the heart. The impacts of acute radiation-induced damage and its chronic effects on normal heart cells are highly relevant in radiotherapy with increasing lifespans of patients. Biomarkers for normal tissue damage after radiation exposure, whether accidental or therapeutic, are being studied as indicators of both acute and delayed effects. Recent research has highlighted the potential importance of RNAs, including messenger RNAs (mRNAs), microRNAs (miRNAs), and long non-coding RNAs (lncRNAs) as biomarkers to assess radiation damage. Understanding changes in mRNA and non-coding RNA expression will elucidate biological pathway changes after radiation. Methods To identify significant expression changes in mRNAs, lncRNAs, and miRNAs, we performed whole transcriptome microarray analysis of mouse heart tissue at 48 h after whole-body irradiation with 1, 2, 4, 8, and 12 Gray (Gy). We also validated changes in specific lncRNAs through RT-qPCR. Ingenuity Pathway Analysis (IPA) was used to identify pathways associated with gene expression changes. Results We observed sustained increases in lncRNAs and mRNAs, across all doses of radiation. Alas2 , Aplnr, and Cxc3r1 were the most significantly downregulated mRNAs across all doses. Among the significantly upregulated mRNAs were cell-cycle arrest biomarkers Gdf15, Cdkn1a, and Ckap2. Additionally, IPA identified significant changes in gene expression relevant to senescence, apoptosis, hemoglobin synthesis, inflammation, and metabolism. LncRNAs Abhd11os, Pvt1, Trp53cor1 , and Dino showed increased expression with increasing doses of radiation. We did not observe any miRNAs with sustained up- or downregulation across all doses, but miR-149-3p, miR-6538, miR-8101, miR-7118-5p, miR-211-3p, and miR-3960 were significantly upregulated after 12 Gy. Conclusions Radiation-induced RNA expression changes may be predictive of normal tissue toxicities and may indicate targetable pathways for radiation countermeasure development and improved radiotherapy treatment plans.

High-containment laboratories (HCLs) conduct critical research on infectious diseases, provide diagnostic services, and produce vaccines for the world’s most dangerous pathogens, often called high-consequence pathogens (HCPs). The modernization of HCLs has led to an increasingly cyber-connected laboratory infrastructure. The unique cyberphysical elements of these laboratories and the critical data they generate pose cybersecurity concerns specific to these laboratories. Cyberbiosecurity, the discipline devoted to the study of cybersecurity risks in conjunction with biological risks, is a relatively new field for which few approaches have been developed to identify, assess, and mitigate cyber risks in biological research and diagnostic environments. This study provides a novel approach for cybersecurity risk assessment and identification of risk mitigation measures by applying an asset-impact analysis to the unique environment of HCLs. First, we identified the common cyber and cyberphysical systems in HCLs, summarizing the typical cyber-workflow. We then analyzed the potential adverse outcomes arising from a compromise of these cyber and cyberphysical systems, broadly categorizing potential consequences as relevant to scientific advancement, public health, worker safety, security, and the financial wellbeing of these laboratories. Finally, we discussed potential risk mitigation strategies, leaning heavily on the cybersecurity materials produced by the Center for Internet Security (CIS), including the CIS Controls ® , that can serve as a guide for HCL operators to begin the process of implementing risk mitigation measures to reduce their cyberbiorisk and considering the integration of cyber risk management into existing biorisk management practices. This paper provides a discussion to raise awareness among laboratory decision-makers of these critical risks to safety and security within HCLs. Furthermore, this paper can serve as a guide for evaluating cyberbiorisks specific to a laboratory by identifying cyber-connected assets and the impacts associated with a compromise of those assets.

Somatic sensation is defined by the existence of a diversity of primary sensory neurons with unique biological features and response profiles to external and internal stimuli. However, there is no coherent picture about how this diversity of cell states is transcriptionally generated. Here, we use deep single cell analysis to resolve fate splits and molecular biasing processes during sensory neurogenesis in mice. Our results identify a complex series of successive and specific transcriptional changes in post-mitotic neurons that delineate hierarchical regulatory states leading to the generation of the main sensory neuron classes. In addition, our analysis identifies previously undetected early gene modules expressed long before fate determination although being clearly associated with defined sensory subtypes. Overall, the early diversity of sensory neurons is generated through successive bi-potential intermediates in which synchronization of relevant gene modules and concurrent repression of competing fate programs precede cell fate stabilization and final commitment. The diversity of primary sensory neurons and how fate choice is determined is unclear. Here, the authors use single cell RNA sequencing analysis of early murine somatosensory neurons to show that sensory neuron diversity is achieved by a transition through a bi-potential intermediate state.

Significance: Time-domain functional near-infrared spectroscopy (TD-fNIRS) has been considered as the gold standard of noninvasive optical brain imaging devices. However, due to the high cost, complexity, and large form factor, it has not been as widely adopted as continuous wave NIRS systems. Aim: Kernel Flow is a TD-fNIRS system that has been designed to break through these limitations by maintaining the performance of a research grade TD-fNIRS system while integrating all of the components into a small modular device. Approach: The Kernel Flow modules are built around miniaturized laser drivers, custom integrated circuits, and specialized detectors. The modules can be assembled into a system with dense channel coverage over the entire head. Results: We show performance similar to benchtop systems with our miniaturized device as characterized by standardized tissue and optical phantom protocols for TD-fNIRS and human neuroscience results. Conclusions: The miniaturized design of the Kernel Flow system allows for broader applications of TD-fNIRS.