Deep Learning for Automated Experimentation in Scanning Transmission Electron Microscopy

Machine learning (ML) has become critical for post-acquisition data analysis in (scanning) transmission electron microscopy, (S)TEM, imaging and spectroscopy. An emerging trend is the transition to real-time analysis and closed-loop microscope operation. The effective use of ML in electron microscopy now requires the development of strategies for microscopy-centered experiment workflow design and optimization. Here, we discuss the associated challenges with the transition to active ML, including sequential data analysis and out-of-distribution drift effects, the requirements for the edge operation, local and cloud data storage, and theory in the loop operations. Specifically, we discuss the relative contributions of human scientists and ML agents in the ideation, orchestration, and execution of experimental workflows and the need to develop universal hyper languages that can apply across multiple platforms. These considerations will collectively inform the operationalization of ML in next-generation experimentation.Comment: Review Articl

Composite Scalars at LEP: Constraining Technicolor Theories

LEPI and LEPII data can be used to constrain technicolor models with light, neutral pseudo-Nambu-Goldstone bosons, Pa. We use published limits on branching ratios and cross sections for final states with photons, large missing energy, jet pairs, and b bbar pairs to constrain the anomalous Pa Z0 Z0, Pa Z0 photon, and Pa photon photon couplings. From these results, we derive bounds on the size of the technicolor gauge group and the number of technifermion doublets in models such as Low-scale Technicolor.Comment: 27 pages (including title page), 15 figures, 6 tables. version 2: In addressing PRD referee comments, we have significantly expanded our manuscript, to include detailed discussion of limits from LEP II data, as well as expanding the number or specific models to which we apply our results. As a result, we have changed the title from "Z0 decays to composite scalars: constraining technicolor theories

Nontuberculous mycobacterium infection in a burn ICU patient

Infection is a leading cause of mortality in burn patients, typically due to bacterial pathogens such as Staphylococcus aureus and Pseudomonas aeruginosa, less commonly fungi, and possibly viruses. In non-burn patients with underlying pulmonary or cutaneous disease, nontuberculous mycobacteria (NTM) have become an increasingly recognized cause of infection, especially in patients who are immunocompromised. Patients with severe burns might have higher rates of NTM infections due to inherent risks associated with the burn injury: compromised skin integrity, immunocompromised state, inhalation injury, and frequent use of indwelling vascular catheters. To date there have been no reports describing the incidence of mycobacterial infections in burn patients. We describe a case of Mycobacterium abscessus bacteremia and clinical record review of patients admitted to the US Department of Defense burn center with severe burns for other evidence of NTM infections from 1 May 2000 to 30 April 2009

Initial Characterization of the Pig Skin Bacteriome and Its Effect on In Vitro Models of Wound Healing.

Elucidating the roles and composition of the human skin microbiome has revealed a delicate interplay between resident microbes and wound healing. Evolutionarily speaking, normal cutaneous flora likely has been selected for because it potentiates or, at minimum, does not impede wound healing. While pigs are the gold standard model for wound healing studies, the porcine skin microbiome has not been studied in detail. Herein, we performed 16S rDNA sequencing to characterize the pig skin bacteriome at several anatomical locations. Additionally, we used bacterial conditioned-media with in vitro techniques to examine the paracrine effects of bacterial-derived proteins on human keratinocytes (NHEK) and fibroblasts (NHDF). We found that at the phyla level, the pig skin bacteriome is similar to that of humans and largely consists of Firmicutes (55.6%), Bacteroidetes (20.8%), Actinobacteria (13.3%), and Proteobacteria (5.1%) however species-level differences between anatomical locations exist. Studies of bacterial supernatant revealed location-dependent effects on NHDF migration and NHEK apoptosis and growth factor release. These results expand the limited knowledge of the cutaneous bacteriome of healthy swine, and suggest that naturally occurring bacterial flora affects wound healing differentially depending on anatomical location. Ultimately, the pig might be considered the best surrogate for not only wound healing studies but also the cutaneous microbiome. This would not only facilitate investigations into the microbiome's role in recovery from injury, but also provide microbial targets for enhancing or accelerating wound healing