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Abstract Mononuclear phagocytes (MNP), including macrophages and dendritic cells form an essential component of primary responses to environmental hazards and toxic exposures. This is particularly important in disease conditions such as asthma and allergic airway disease, where many different cell types are present. In this study, we differentiated CD34+ haematopoietic stem cells towards different populations of MNP in an effort to understand how different cell subtypes present in inflammatory disease microenvironments respond to the common allergen house dust mite (HDM). Using single cell mRNA sequencing, we demonstrate that macrophage subtypes MCSPP1+ and MLCMARCO+ display different patterns of gene expression after HDM challenge, noted especially for the chemokines CXCL5, CXCL8, CCL5 and CCL15. MLCCD206Hi alternatively activated macrophages displayed the greatest changes in expression, while neutrophil and monocyte populations did not respond. Further work investigated how pollutant diesel exhaust particles could modify these transcriptional responses and revealed that CXC but not CC type chemokines were further upregulated. Through the use of diesel particles with adsorbed material removed, we suggest that soluble pollutants on these particles are the active constituents responsible for the modifying effects on HDM. This study highlights that environmental exposures may influence tissue responses dependent on which MNP cell type is present, and that these should be considerations when modelling such events in vitro. Understanding the nuanced responsiveness of different immune cell types to allergen and pollutant exposure also contributes to a better understanding of how these exposures influence the development and exacerbation of human disease.

Abstract Terahertz time-domain spectroscopy (THz-TDS) can be used to map spatial variations in electrical properties such as sheet conductivity, carrier density, and carrier mobility in graphene. Here, we consider wafer-scale graphene grown on germanium by chemical vapor deposition with non-uniformities and small domains due to reconstructions of the substrate during growth. The THz conductivity spectrum matches the predictions of the phenomenological Drude–Smith model for conductors with non-isotropic scattering caused by backscattering from boundaries and line defects. We compare the charge carrier mean free path determined by THz-TDS with the average defect distance assessed by Raman spectroscopy, and the grain boundary dimensions as determined by transmission electron microscopy. The results indicate that even small angle orientation variations below 5° within graphene grains influence the scattering behavior, consistent with significant backscattering contributions from grain boundaries.

Abstract Myeloid cells form an essential component of initial responses to environmental hazards and toxic exposures. The ability to model these responses in vitro is central to efforts tasked with identifying hazardous materials and understanding mechanisms of injury and disease. Induced pluripotent stem cell (iPSC) derived cells have been suggested as alternatives to more established primary cell testing systems for these purposes. iPSC derived macrophage and dendritic like cells were compared to CD34+ haematopoietic stem cell derived populations using transcriptomic analysis. Using single cell sequencing-based characterisation of iPSC derived myeloid cells, we identified transitional, mature and M2 like macrophages as well as dendritic like antigen presenting cells and fibrocytes. Direct transcriptomic comparisons between iPSC and CD34+ cell derived populations revealed higher expression of myeloid differentiation genes such as MNDA, CSF1R and CSF2RB in CD34+ cells, while iPSC populations had higher fibroblastic and proliferative markers. Exposure of differentiated macrophage populations to nanoparticle alone or in combination with dust mite, resulted in differential gene expression on combination only, with responses markedly absent in iPSC compared to CD34+ derived cells. The lack of responsiveness in iPSC derived cells may be attributable to lower levels of dust mite component receptors CD14, TLR4, CLEC7A and CD36. In summary, iPSC derived myeloid cells display typical characteristics of immune cells but may lack a fully mature phenotype to adequately respond to environmental exposures.

Abstract Duodenal neuroendocrine neoplasms (dNENs) are rare neoplasms but their incidence is on the rise. They are classified into 5 sub-types but there remains much heterogeneity in behaviour in particular of non-functioning dNENs. To retrospectively analyse outcomes for all types of dNENs, and highlight prognostic factors associated with worse outcome. 102 (57 m/45f.) patients were identified with mean age at diagnosis 62 (range 32–87) years. The majority were non-functioning tumours 87/102 and median size was 10 mm (range 0.9–130 mm). 83 patients had Stage I or II disease, of which 17 underwent endoscopic resection with R1 rate of 45% and complication rate 12%. 36 patients were kept under endoscopic surveillance. There were 11 deaths of which 4 were disease related. Age and Ki67 > 20% were associated with worse OS in all dNENs. In non-functioning dNENs Ki67 > 3% was a predictor of lymph nodes metastases with OR 18.2 (2.54–13) (p

Myeloid cells form an essential component of initial responses to environmental hazards and toxic exposures. The ability to model these responses in vitro is central to efforts tasked with identifying hazardous materials and understanding mechanisms of injury and disease. Induced pluripotent stem cell (iPSC) derived cells have been suggested as alternatives to more established primary cell testing systems for these purposes. iPSC derived macrophage and dendritic like cells were compared to CD34+ haematopoietic stem cell derived populations using transcriptomic analysis. Using single cell sequencing-based characterisation of iPSC derived myeloid cells, we identified transitional, mature and M2 like macrophages as well as dendritic like antigen presenting cells and fibrocytes. Direct transcriptomic comparisons between iPSC and CD34+ cell derived populations revealed higher expression of myeloid differentiation genes such as MNDA, CSF1R and CSF2RB in CD34+ cells, while iPSC populations had higher fibroblastic and proliferative markers. Exposure of differentiated macrophage populations to nanoparticle alone or in combination with dust mite, resulted in differential gene expression on combination only, with responses markedly absent in iPSC compared to CD34+ derived cells. The lack of responsiveness in iPSC derived cells may be attributable to lower levels of dust mite component receptors CD14, TLR4, CLEC7A and CD36. In summary, iPSC derived myeloid cells display typical characteristics of immune cells but may lack a fully mature phenotype to adequately respond to environmental exposures. [ABSTRACT FROM AUTHOR]

Abstract We provide a descriptive characterization of the unfolded protein response (UPR) in skeletal muscle of human patients with peritoneal sepsis and a sepsis model of C57BL/6J mice. Patients undergoing open surgery were included in a cross-sectional study and blood and skeletal muscle samples were taken. Key markers of the UPR and cluster of differentiation 68 (CD68) as surrogate of inflammatory injury were evaluated by real-time PCR and histochemical staining. CD68 mRNA increased with sepsis in skeletal muscle of patients and animals (p

Many nocturnally active fireflies use precisely timed bioluminescent patterns to identify mates, making them especially vulnerable to light pollution. As urbanization continues to brighten the night sky, firefly populations are under constant stress, and close to half of the species are now threatened. Ensuring the survival of firefly biodiversity depends on a large-scale conservation effort to monitor and protect thousands of populations. While species can be identified by their flash patterns, current methods require expert measurement and manual classification and are infeasible given the number and geographic distribution of fireflies. Here we present the application of a recurrent neural network (RNN) for accurate automated firefly flash pattern classification. Using recordings from commodity cameras, we can extract flash trajectories of individuals within a swarm and classify their species with an accuracy of approximately seventy percent. In addition to its potential in population monitoring, automated classification provides the means to study firefly behavior at the population level. We employ the classifier to measure and characterize the variability within and between swarms, unlocking a new dimension of their behavior. Our method is open source, and deployment in community science applications could revolutionize our ability to monitor and understand firefly populations., (© 2024. The Author(s).)

The spatial distributions of populations are both influenced by local variables and by characteristics of surrounding landscapes. Understanding how landscape features spatially structure the frequency of a trait in a population, the abundance of a species or the species' richness remains difficult specially because the spatial scale effects of the landscape variables are unknown. Various methods have been proposed but their results are not easily comparable. Here, we introduce "siland", a general method for analyzing the effect of landscape features. Based on a sequential procedure of maximum likelihood estimation, it simultaneously estimates the spatial scales and intensities of landscape variable effects. It does not require any information about the scale of effect. It integrates two landscape effects models: one is based on focal sample site (Bsiland, b for buffer) and one is distance weighted using Spatial Influence Function (Fsiland, f for function). We implemented "siland" in the adaptable and user-friendly R eponym package. It performs landscape analysis on georeferenced point observations (described in a Geographic Information System shapefile format) and allows for effects tests, effects maps and models comparison. We illustrated its use on a real dataset by the study of a crop pest (codling moth densities).