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Background Local priming of the innate immune system with a Toll-like receptor (TLR)2/6 agonist may reduce morbidity and mortality associated with viral respiratory tract infections, particularly for the elderly and those with chronic diseases. The objectives of the present study were to understand the potential of prophylactic treatment with a TLR2/6 agonist as an enhancer of innate immunity pathways leading to accelerated respiratory virus clearance from the upper airways. Methods Two randomised, double-blind, placebo-controlled clinical trials were conducted in healthy adult participants. The first dose-escalation study assessed safety, tolerability and mechanistic biomarkers following single and repeated intranasal administrations of INNA-051. The second was an influenza A viral challenge study assessing the impact of treatment on host defence biomarkers and viral load. Results INNA-051 was well tolerated in both studies, with no dose-limiting toxicities identified. Mechanistic biomarkers assessed in both studies demonstrated the expected engagement of pharmacology, including innate immune pathways. There were lower than anticipated rates of infection. Post hoc analysis conducted in laboratory-confirmed infected participants with low or no antibody titre against the challenge virus showed INNA-051 treatment led to a significantly shorter duration of infection and increased expression of genes and pathways associated with host defence responses against influenza. Conclusions The safety and pharmacology profile of INNA-051 confirms preclinical studies. INNA-051 increased expression of genes and pathways associated with host defence responses against influenza and was associated with a shorter duration of infection. These studies support further clinical assessment in the context of natural viral respiratory tract infections in individuals at increased risk of severe illness.

Open records laws have a significant impact on how college athletic departments operate and compete. This paper examines the current state of open records laws in relation to college athletics departments. The central finding is that the current set of incentives creates an untenable “race to the bottom” which undermines the public policy goals of open records laws. The topic of open records laws is significant for athletics department employees and other stakeholders. The foundational principles underlying open records laws can be traced back to the founding of the United States and are a critical aspect of democracy. However, athletic departments use a variety of tactics to avoid or delay providing documents in response to public records requests, and these tactics vary by state and institution. The incentives faced by coaches and athletics directors contribute to a situation where these stakeholders and lawmakers advocate for policy changes that ultimately undermine the public’s ability to access records relating to intercollegiate sport. The solution to this problem will require cooperation across states to enact a set of model rules that will level the playing field and restore the public’s ability to access documents produced by college athletics departments.

Designing machine learning hardware on flexible substrates is promising for several applications. Here, the authors propose an integrated smart system built with low-cost flexible electronics components for classifying human malodour, and demonstrates that the proposed system scores malodour as good as expert human assessors.

Abstract Background Immunofluorescent confocal microscopy uses labeled antibodies as probes against specific macromolecules to discriminate between multiple cell types. For images of the developmental mouse lung, these cells are themselves organized into densely packed higher-level anatomical structures. These types of images can be challenging to segment automatically for several reasons, including the relevance of biomedical context, dependence on the specific set of probes used, prohibitive cost of generating labeled training data, as well as the complexity and dense packing of anatomical structures in the image. The use of an application ontology helps surmount these challenges by combining image data with its metadata to provide a meaningful biological context, modeled after how a human expert would make use of contextual information to identify histological structures, that constrains and simplifies the process of segmentation and object identification. Results We propose an innovative approach for the semi-supervised analysis of complex and densely packed anatomical structures from immunofluorescent images that utilizes an application ontology to provide a simplified context for image segmentation and object identification. We describe how the logical organization of biological facts in the form of an ontology can provide useful constraints that facilitate automatic processing of complex images. We demonstrate the results of ontology-guided segmentation and object identification in mouse developmental lung images from the Bioinformatics REsource ATlas for the Healthy lung database of the Molecular Atlas of Lung Development (LungMAP1) program Conclusion We describe a novel ontology-guided approach to segmentation and classification of complex immunofluorescence images of the developing mouse lung. The ontology is used to automatically generate constraints for each image based on its biomedical context, which facilitates image segmentation and classification.

An important challenge for primary or secondary analysis of cytometry data is how to facilitate productive collaboration between domain and quantitative experts. Domain experts in cytometry laboratories and core facilities increasingly recognize the need for automated workflows in the face of increasing data complexity, but by and large, still conduct all analysis using traditional applications, predominantly FlowJo. To a large extent, this cuts domain experts off from the rapidly growing library of Single Cell Data Science algorithms available, curtailing the potential contributions of these experts to the validation and interpretation of results. To address this challenge, we developed FlowKit, a Gating-ML 2.0-compliant Python package that can read and write FCS files and FlowJo workspaces. We present examples of the use of FlowKit for constructing reporting and analysis workflows, including round-tripping results to and from FlowJo for joint analysis by both domain and quantitative experts.

Identifying the locations of hydrothermal chimneys across mapped areas of seafloor spreading ridges unlocks the ability to research questions about their correlations to geology, the cooling of the lithosphere, and deep-sea biogeography. We developed a Chimney Identification Tool (CIT) that utilizes a Convolutional Neural Network (CNN) to classify 1 m gridded AUV bathymetry and identify the locations of hydrothermal vent chimneys. A CNN is a type of Machine-Learning model that is able to classify raster data based on the shapes and textures in the input, making it ideal for this task. The criteria that have been used in previous manual classifications of chimneys have focused on the round base and spire shape of the features, and are not easily quantifiable. Machine-Learning techniques have previously been implemented with sonar data to classify seafloor geology, but this is the first application of these methods to hydrothermal systems. In developing the CIT, we compiled the bathymetry data from two rasters from the Endeavor Ridge—each gridded at a 1 m resolution—containing 34 locations of known hydrothermal chimneys, and from the 92° W segment of the Galapagos Spreading Center (GSC) containing 14. The CIT produced a primary group of outputs with 96% agreement with the manual classification; moreover, it correctly caught 29 of the 34 known chimneys from Endeavor and 10 of the 14 from the GSC. The CIT is trained to identify features with the characteristic shape of a hydrothermal vent chimney; therefore, it is susceptible to the misclassification of unusually shaped cases, given the limited training data. As a result, to provide the option of having a more inclusive application, the CIT also produced a secondary group of output locations with 61% agreement with the manual classification; moreover, it caught three of the four additional known chimneys from the GSC and four of the five from Endeavor. The CIT will be used in future investigations where an inventory of individual chimneys is important, such as the cataloguing of off-axis hydrothermal venting and the investigation of chimney distribution in connection to seafloor eruptions.

Background: Several techniques are under investigation to improve the immunogenicity of HIV-1 DNA vaccine candidates. DNA vaccines are advantageous due to their ease of design, expression of multiple antigens, and safety. Methods: The HVTN 098 trial assessed the PENNVAX®-GP DNA vaccine (encoding HIV env, gag, pol) administered with or without plasmid IL-12 at 0-, 1-, 3-, and 6-month timepoints via intradermal (ID) or intramuscular (IM) electroporation (EP) in healthy, adult participants. We report on safety, tolerability, and acceptability. Results: HVTN 098 enrolled 94 participants: 85 received PENNVAX®-GP and nine received placebo. Visual analog scale (VAS) pain scores immediately after each vaccination were lower in the ID/EP than in the IM/EP group (medians 4.1–4.6 vs. 6–6.5, p < 0.01). IM/EP participants reported greater pain and/or tenderness at the injection site. Most ID/EP participants had skin lesions such as scabs/eschars, scars, and pigmentation changes, which resolved within 6 months in 51% of participants (24/55). Eighty-two percent of IM/EP and 92% of ID/EP participant survey responses showed acceptable levels of discomfort. Conclusions: ID/EP and IM/EP are distinct experiences; however, HIV-1 DNA vaccination by either route was safe, tolerable and acceptable by most study participants.

Existing normative flow cytometry data have several limitations including small sample sizes, incompletely described study populations, variable flow cytometry methodology, and limited depth for defining lymphocyte subpopulations. To overcome these issues, we defined high-dimensional flow cytometry reference ranges for the healthy human immune system using Human Immunology Project Consortium methodologies after carefully screening 127 subjects deemed healthy through clinical and laboratory testing. We enrolled subjects in the following age cohorts: 18-29 years, 30-39, 40-49, and 50-66 and enrolled cohorts to ensure an even gender distribution and at least 30% non-Caucasians. From peripheral blood mononuclear cells, flow cytometry reference ranges were defined for >50 immune subsets including T-cell (activation, maturation, T follicular helper and regulatory T cell), B-cell, and innate cells. We also developed a web tool for visualization of the dataset and download of raw data. This dataset provides the immunology community with a resource to compare and extract data from rigorously characterized healthy subjects across age groups, gender and race.

Manual analysis of flow cytometry data and subjective gate-border decisions taken by individuals continue to be a source of variation in the assessment of antigen-specific T cells when comparing data across laboratories, and also over time in individual labs. Therefore, strategies to provide automated analysis of major histocompatibility complex (MHC) multimer-binding T cells represent an attractive solution to decrease subjectivity and technical variation. The challenge of using an automated analysis approach is that MHC multimer-binding T cell populations are often rare and therefore difficult to detect. We used a highly heterogeneous dataset from a recent MHC multimer proficiency panel to assess if MHC multimer-binding CD8+ T cells could be analyzed with computational solutions currently available, and if such analyses would reduce the technical variation across different laboratories. We used three different methods, FLOw Clustering without K (FLOCK), Scalable Weighted Iterative Flow-clustering Technique (SWIFT), and ReFlow to analyze flow cytometry data files from 28 laboratories. Each laboratory screened for antigen-responsive T cell populations with frequency ranging from 0.01 to 1.5% of lymphocytes within samples from two donors. Experience from this analysis shows that all three programs can be used for the identification of high to intermediate frequency of MHC multimer-binding T cell populations, with results very similar to that of manual gating. For the less frequent populations (

BACKGROUND/OBJECTIVES: Pain-related misbeliefs among health care professionals (HCPs) are common and contribute to ineffective postoperative pain assessment. While standardized patients (SPs) have been effectively used to improve HCPs’ assessment skills, not all centres have SP programs. The present equivalence randomized controlled pilot trial examined the efficacy of an alternative simulation method – deteriorating patient-based simulation (DPS) – versus SPs for improving HCPs’ pain knowledge and assessment skills.

With the recent results of promising cancer vaccines and immunotherapy 1 – 5 , immune monitoring has become increasingly relevant for measuring treatment-induced effects on T cells, and an essential tool for shedding light on the mechanisms responsible for a successful treatment. Flow cytometry is the canonical multi-parameter assay for the fine characterization of single cells in solution, and is ubiquitously used in pre-clinical tumor immunology and in cancer immunotherapy trials. Current state-of-the-art polychromatic flow cytometry involves multi-step, multi-reagent assays followed by sample acquisition on sophisticated instruments capable of capturing up to 20 parameters per cell at a rate of tens of thousands of cells per second. Given the complexity of flow cytometry assays, reproducibility is a major concern, especially for multi-center studies. A promising approach for improving reproducibility is the use of automated analysis borrowing from statistics, machine learning and information visualization 21 – 23 , as these methods directly address the subjectivity, operator-dependence, labor-intensive and low fidelity of manual analysis. However, it is quite time-consuming to investigate and test new automated analysis techniques on large data sets without some centralized information management system. For large-scale automated analysis to be practical, the presence of consistent and high-quality data linked to the raw FCS files is indispensable. In particular, the use of machine-readable standard vocabularies to characterize channel metadata is essential when constructing analytic pipelines to avoid errors in processing, analysis and interpretation of results. For automation, this high-quality metadata needs to be programmatically accessible, implying the need for a consistent Application Programming Interface (API). In this manuscript, we propose that upfront time spent normalizing flow cytometry data to conform to carefully designed data models enables automated analysis, potentially saving time in the long run. The ReFlow informatics framework was developed to address these data management challenges.