Sources of Airborne Endotoxins in Ambient Air and Exposure of Nearby Communities—A Review

Endotoxin is a bioaerosol component that is known to cause respiratory effects in exposed populations. To date, most research focused on occupational exposure, whilst much less is known about the impact of emissions from industrial operations on downwind endotoxin concentrations. A review of the literature was undertaken, identifying studies that reported endotoxin concentrations in both ambient environments and around sources with high endotoxin emissions. Ambient endotoxin concentrations in both rural and urban areas are generally below 10 endotoxin units (EU) m−3; however, around significant sources such as compost facilities, farms, and wastewater treatment plants, endotoxin concentrations regularly exceeded 100 EU m−3. However, this is affected by a range of factors including sampling approach, equipment, and duration. Reported downwind measurements of endotoxin demonstrate that endotoxin concentrations can remain above upwind concentrations. The evaluation of reported data is complicated due to a wide range of different parameters including sampling approaches, temperature, and site activity, demonstrating the need for a standardised methodology and improved guidance. Thorough characterisation of ambient endotoxin levels and modelling of endotoxin from pollution sources is needed to help inform future policy and support a robust health-based risk assessment process

A Controlled Study on the Characterisation of Bioaerosols Emissions from Compost

Bioaerosol emissions arising from biowaste treatment are an issue of public concern. To better characterise the bioaerosols, and to assess a range of measurement methods, we aerosolised green waste compost under controlled conditions. Viable and non-viable Andersen samplers, cyclone samplers and a real time bioaerosol detection system (Spectral Intensity Bioaerosol Sensor (SIBS)) were deployed simultaneously. The number-weighted fraction of fluorescent particles was in the range 22–26% of all particles for low and high emission scenarios. Overall fluorescence spectral profiles seen by the SIBS exhibited several peaks across the 16 wavelength bands from 298 to 735 nm. The size-fractionated endotoxin profile showed most endotoxin resided in the 2.1–9 μm aerodynamic diameter fraction, though up to 27% was found in a finer size fraction. A range of microorganisms were detected through culture, Matrix Assisted Laser Desorption and Ionisation Time of Flight Mass Spectrometry (MALDI-TOF) and quantitative polymerase chain reaction (qPCR), including Legionella pneumophila serogroup 1. These findings contribute to our knowledge of the physico-chemical and biological characteristics of bioaerosols from composting sites, as well as informing future monitoring approaches and data interpretation for bioaerosol measurement

Unlocking Accuracy and Fairness in Differentially Private Image Classification

Privacy-preserving machine learning aims to train models on private data without leaking sensitive information. Differential privacy (DP) is considered the gold standard framework for privacy-preserving training, as it provides formal privacy guarantees. However, compared to their non-private counterparts, models trained with DP often have significantly reduced accuracy. Private classifiers are also believed to exhibit larger performance disparities across subpopulations, raising fairness concerns. The poor performance of classifiers trained with DP has prevented the widespread adoption of privacy preserving machine learning in industry. Here we show that pre-trained foundation models fine-tuned with DP can achieve similar accuracy to non-private classifiers, even in the presence of significant distribution shifts between pre-training data and downstream tasks. We achieve private accuracies within a few percent of the non-private state of the art across four datasets, including two medical imaging benchmarks. Furthermore, our private medical classifiers do not exhibit larger performance disparities across demographic groups than non-private models. This milestone to make DP training a practical and reliable technology has the potential to widely enable machine learning practitioners to train safely on sensitive datasets while protecting individuals' privacy

Pyocyanin-Enhanced Neutrophil Extracellular Trap Formation Requires the NADPH Oxidase

Beyond intracellular killing, a novel neutrophil-based antimicrobial mechanism has been recently discovered: entrapment and killing by neutrophil extracellular traps (NETs). NETs consist of extruded nuclear DNA webs decorated with granule proteins. Although NET formation is an important innate immune mechanism, uncontrolled NET release damages host tissues and has been linked to several diseases including cystic fibrosis (CF). The major CF airway pathogen Pseudomonas aeruginosa establishes chronic infection. Pseudomonas imbedded within biofilms is protected against the immune system, but maintains chronic inflammation that worsens disease symptoms. Aberrant NET release from recruited neutrophils was found in CF, but the underlying mechanisms remain unclear. One of the most important Pseudomonas virulence factors is pyocyanin, a redox-active pigment that has been associated with diminished lung function in CF. Here we show that pyocyanin promotes NET formation in a time- and dose-dependent manner. Most CF Pseudomonas clinical isolates tested produce pyocyanin in vitro. Pyocyanin-derived reactive oxygen species are required for its NET release. Inhibitor experiments demonstrated involvement of Jun N-terminal Kinase (JNK) and phosphatidylinositol 3-Kinase (PI3K) in pyocyanin-induced NET formation. Pyocyanin-induced NETs also require the NADPH oxidase because NET release in chronic granulomatous disease neutrophils was greatly reduced. Comparison of neutrophils from gp91phox- and p47phox-deficient patients revealed that pyocyanin-triggered NET formation is proportional to their residual superoxide production. Our studies identify pyocyanin as the first secreted bacterial toxin that enhances NET formation. The involvement of NADPH oxidase in pyocyanin-induced NET formation represents a novel mechanism of pyocyanin toxicity

Validation of Electroencephalographic Recordings Obtained with a Consumer-Grade, Single Dry Electrode, Low-Cost Device: A Comparative Study

The functional validity of the signal obtained with low-cost electroencephalography (EEG) devices is still under debate. Here, we have conducted an in-depth comparison of the EEG-recordings obtained with a medical-grade golden-cup electrodes ambulatory device, the SOMNOwatch + EEG-6, vs those obtained with a consumer-grade, single dry electrode low-cost device, the NeuroSky MindWave, one of the most a ordable devices currently available. We recorded EEG signals at Fp1 using the two di erent devices simultaneously on 21 participants who underwent two experimental phases: a 12-minute resting state task (alternating two cycles of closed/open eyes periods), followed by 60-minute virtual-driving task. We evaluated the EEG recording quality by comparing the similarity between the temporal data series, their spectra, their signal-to-noise ratio, the reliability of EEG measurements (comparing the closed eyes periods), as well as their blink detection rate. We found substantial agreement between signals: whereas, qualitatively, the NeuroSky MindWave presented higher levels of noise and a biphasic shape of blinks, the similarity metric indicated that signals from both recording devices were significantly correlated. While the NeuroSky MindWave was less reliable, both devices had a similar blink detection rate. Overall, the NeuroSky MindWave is noise-limited, but provides stable recordings even through long periods of time. Furthermore, its data would be of adequate quality compared to that of conventional wet electrode EEG devices, except for a potential calibration error and spectral differences at low frequencies.Spanish Department of Transportation, Madrid, Spain (Grant No. SPIP2014-1426 to L.L.D.S.)A.C. is funded by a Spanish Ministry of Economy and Competitiveness grant (PSI2016-80558-R to A.C.)S.R. is funded by an Andalusian Government Excellence Research grant (P11-TIC-7983)L.J.F. is funded by a Spanish Ministry of Economy and Competitiveness grant (PSI2014-53427-P) and a Fundación Séneca grant (19267/PI/14)L.L.D.S. is currently supported by the Ramón y Cajal fellowship program (RYC-2015-17483)C.D.-P. is currently supported by the CEIMAR program (CEIMAR2018-2)C.D.-P. and L.L.D.S. are supported by a Santander Bank—CEMIX UGR-MADOC grant (Project PINS 2018-15

Toward an integrative understanding of social behavior: new models and new opportunities.

Social interactions among conspecifics are a fundamental and adaptively significant component of the biology of numerous species. Such interactions give rise to group living as well as many of the complex forms of cooperation and conflict that occur within animal groups. Although previous conceptual models have focused on the ecological causes and fitness consequences of variation in social interactions, recent developments in endocrinology, neuroscience, and molecular genetics offer exciting opportunities to develop more integrated research programs that will facilitate new insights into the physiological causes and consequences of social variation. Here, we propose an integrative framework of social behavior that emphasizes relationships between ultimate-level function and proximate-level mechanism, thereby providing a foundation for exploring the full diversity of factors that underlie variation in social interactions, and ultimately sociality. In addition to identifying new model systems for the study of human psychopathologies, this framework provides a mechanistic basis for predicting how social behavior will change in response to environmental variation. We argue that the study of non-model organisms is essential for implementing this integrative model of social behavior because such species can be studied simultaneously in the lab and field, thereby allowing integration of rigorously controlled experimental manipulations with detailed observations of the ecological contexts in which interactions among conspecifics occur