A novel approach for estimating lung tumor motion based on dynamic features in 4D-CT.

Due to the high expenses involved, 4D-CT data for certain patients may only include five respiratory phases (0%, 20%, 40%, 60%, and 80%). This limitation can affect the subsequent planning of radiotherapy due to the absence of lung tumor information for the remaining five respiratory phases (10%, 30%, 50%, 70%, and 90%). This study aims to develop an interpolation method that can automatically derive tumor boundary contours for the five omitted phases using the available 5-phase 4D-CT data. The dynamic mode decomposition (DMD) method is a data-driven and model-free technique that can extract dynamic information from high-dimensional data. It enables the reconstruction of long-term dynamic patterns using only a limited number of time snapshots. The quasi-periodic motion of a deformable lung tumor caused by respiratory motion makes it suitable for treatment using DMD. The direct application of the DMD method to analyze the respiratory motion of the tumor is impractical because the tumor is three-dimensional and spans multiple CT slices. To predict the respiratory movement of lung tumors, a method called uniform angular interval (UAI) sampling was developed to generate snapshot vectors of equal length, which are suitable for DMD analysis. The effectiveness of this approach was confirmed by applying the UAI-DMD method to the 4D-CT data of ten patients with lung cancer. The results indicate that the UAI-DMD method effectively approximates the lung tumor's deformable boundary surface and nonlinear motion trajectories. The estimated tumor centroid is within 2 mm of the manually delineated centroid, a smaller margin of error compared to the traditional BSpline interpolation method, which has a margin of 3 mm. This methodology has the potential to be extended to reconstruct the 20-phase respiratory movement of a lung tumor based on dynamic features from 10-phase 4D-CT data, thereby enabling more accurate estimation of the planned target volume (PTV).

PreK-12 school and citywide wastewater monitoring of the enteric viruses astrovirus, rotavirus, and sapovirus.

Wastewater monitoring is an efficient and effective way to surveil for various pathogens in communities. This is especially beneficial in areas of high transmission, such as preK-12 schools, where infections may otherwise go unreported. In this work, we apply wastewater disease surveillance using school and community wastewater from across Houston, Texas to monitor three major enteric viruses: astrovirus, sapovirus genogroup GI, and group A rotavirus. We present the results of a 10-week study that included the analysis of 164 wastewater samples for astrovirus, rotavirus, and sapovirus in 10 preK-12 schools, 6 wastewater treatment plants, and 2 lift stations using newly designed RT-ddPCR assays. We show that the RT-ddPCR assays were able to detect astrovirus, rotavirus, and sapovirus in school, lift station, and wastewater treatment plant (WWTP) wastewater, and that a positive detection of a virus in a school sample was paired with a positive detection of the same virus at a downstream lift station or wastewater treatment plant over 97 % of the time. Additionally, we show how wastewater detections of rotavirus in schools and WWTPs were significantly associated with citywide viral intestinal infections. School wastewater can play a role in the monitoring of enteric viruses and in the detection of outbreaks, potentially allowing public health officials to quickly implement mitigation strategies to prevent viral spread into surrounding communities.

Feasibility and applicability of self-sampling based online cervical cancer screening: findings from the China online cervical cancer screening trial.

OBJECTIVE: This study aims to evaluate the feasibility and applicability of an online cervical cancer screening program using a website as the public platform and self-collected HPV testing as the primary screening method. METHODS: A website (mcareu.com) was developed to facilitate the online cervical cancer screening program by Peking University Shenzhen Hospital (PUSH). Women in Shenzhen could register for participation on the website by providing essential demographic data. Sampling kits and specimens were delivered through regular logistics. Eligible women collected vaginal samples by themselves using the provided kits and in referring of the graphic guidance. The specimens were tested for HPV at PUSH or a reference lab, and the results were accessible on the website through participants' personal accounts. Participants who tested positive for high-risk HPV were scheduled for colposcopy and biopsies. The demographic and social background data of the eligible participants were analyzed to evaluate the feasibility and applicability of the online screening approach. RESULTS: A total of 1712 applicants registered for participation, with 99.9% (1710/1712) completing registration with full data. The analysis included 1560 applicants aged 30-59, with an average age of 41.1 (± 7.6) years. Among them, 83.3% (1299/1560) provided self-collected samples for testing. Age-group analysis revealed an overall sample provision rate (SPR) exceeded 80% in all age groups. A significant difference in SPR was observed only between the 30-34 and 45-49 age groups (p < 0.05), while no significant differences were found among other age groups. 99.7% of the samples were tested qualified, and there was no significant difference in sampling failure rate among age groups. Analysis of demographic and social elements showed no significant impact on the rates of sample provision among groups in most of the social elements but the medical insurance and the monthly family-incomes. CONCLUSION: The findings demonstrate that online cervical cancer screening is reliable for self-registration, self-sampling, and self-ordering for specimen transportation. It is suitable for women of all ages needing to be screened, irrespective of social elements, and effectively facilitates screening for women with limited access to medical resources. Therefore, online screening holds promise as an effective approach to increase screening coverage.

Effective data-driven collective variables for free energy calculations from metadynamics of paths.

A variety of enhanced sampling (ES) methods predict multidimensional free energy landscapes associated with biological and other molecular processes as a function of a few selected collective variables (CVs). The accuracy of these methods is crucially dependent on the ability of the chosen CVs to capture the relevant slow degrees of freedom of the system. For complex processes, finding such CVs is the real challenge. Machine learning (ML) CVs offer, in principle, a solution to handle this problem. However, these methods rely on the availability of high-quality datasets-ideally incorporating information about physical pathways and transition states-which are difficult to access, therefore greatly limiting their domain of application. Here, we demonstrate how these datasets can be generated by means of ES simulations in trajectory space via the metadynamics of paths algorithm. The approach is expected to provide a general and efficient way to generate efficient ML-based CVs for the fast prediction of free energy landscapes in ES simulations. We demonstrate our approach with two numerical examples, a 2D model potential and the isomerization of alanine dipeptide, using deep targeted discriminant analysis as our ML-based CV of choice.

Deciphering the virome of Chunkung (Cnidium officinale) showing dwarfism-like symptoms via a high-throughput sequencing analysis.

BACKGROUND: Viruses have notable effects on agroecosystems, wherein they can adversely affect plant health and cause problems (e.g., increased biosecurity risks and economic losses). However, our knowledge of their diversity and interactions with specific host plants in ecosystems remains limited. To enhance our understanding of the roles that viruses play in agroecosystems, comprehensive analyses of the viromes of a wide range of plants are essential. High-throughput sequencing (HTS) techniques are useful for conducting impartial and unbiased investigations of plant viromes, ultimately forming a basis for generating further biological and ecological insights. This study was conducted to thoroughly characterize the viral community dynamics in individual plants. RESULTS: An HTS-based virome analysis in conjunction with proximity sampling and a tripartite network analysis were performed to investigate the viral diversity in chunkung (Cnidium officinale) plants. We identified 61 distinct chunkung plant-associated viruses (27 DNA and 34 RNA viruses) from 21 known genera and 6 unclassified genera in 14 known viral families. Notably, 12 persistent viruses (7 DNA and 5 RNA viruses) were exclusive to dwarfed chunkung plants. The detection of viruses from the families Partitiviridae, Picobirnaviridae, and Spinareoviridae only in the dwarfed plants suggested that they may contribute to the observed dwarfism. The co-infection of chunkung by multiple viruses is indicative of a dynamic and interactive viral ecosystem with significant sequence variability and evidence of recombination. CONCLUSIONS: We revealed the viral community involved in chunkung. Our findings suggest that chunkung serves as a significant reservoir for a variety of plant viruses. Moreover, the co-infection rate of individual plants was unexpectedly high. Future research will need to elucidate the mechanisms enabling several dozen viruses to co-exist in chunkung. Nevertheless, the important insights into the chunkung virome generated in this study may be relevant to developing effective plant viral disease management and control strategies.

Convolutional neural networks develop major organizational principles of early visual cortex when enhanced with retinal sampling.

Primate visual cortex exhibits key organizational principles: cortical magnification, eccentricity-dependent receptive field size and spatial frequency tuning as well as radial bias. We provide compelling evidence that these principles arise from the interplay of the non-uniform distribution of retinal ganglion cells, and a quasi-uniform convergence rate from the retina to the cortex. We show that convolutional neural networks outfitted with a retinal sampling layer, which resamples images according to retinal ganglion cell density, develop these organizational principles. Surprisingly, our results indicate that radial bias is spatial-frequency dependent and only manifests for high spatial frequencies. For low spatial frequencies, the bias shifts towards orthogonal orientations. These findings introduce a novel hypothesis about the origin of radial bias. Quasi-uniform convergence limits the range of spatial frequencies (in retinal space) that can be resolved, while retinal sampling determines the spatial frequency content throughout the retina.

Mix-method toolbox for monitoring greenhouse gas production and microbiome responses to soil amendments.

In this study, we adopt an interdisciplinary approach, integrating agronomic field experiments with soil chemistry, molecular biology techniques, and statistics to investigate the impact of organic residue amendments, such as vinasse (a by-product of sugarcane ethanol production), on soil microbiome and greenhouse gas (GHG) production. The research investigates the effects of distinct disturbances, including organic residue application alone or combined with inorganic N fertilizer on the environment. The methods assess soil microbiome dynamics (composition and function), GHG emissions, and plant productivity. Detailed steps for field experimental setup, soil sampling, soil chemical analyses, determination of bacterial and fungal community diversity, quantification of genes related to nitrification and denitrification pathways, measurement and analysis of gas fluxes (N2O, CH4, and CO2), and determination of plant productivity are provided. The outcomes of the methods are detailed in our publications (Lourenço et al., 2018a; Lourenço et al., 2018b; Lourenço et al., 2019; Lourenço et al., 2020). Additionally, the statistical methods and scripts used for analyzing large datasets are outlined. The aim is to assist researchers by addressing common challenges in large-scale field experiments, offering practical recommendations to avoid common pitfalls, and proposing potential analyses, thereby encouraging collaboration among diverse research groups.•Interdisciplinary methods and scientific questions allow for exploring broader interconnected environmental problems.•The proposed method can serve as a model and protocol for evaluating the impact of soil amendments on soil microbiome, GHG emissions, and plant productivity, promoting more sustainable management practices.•Time-series data can offer detailed insights into specific ecosystems, particularly concerning soil microbiota (taxonomy and functions).

Children's subjective uncertainty-driven sampling behaviour.

Are children and adults sensitive to gaps in their knowledge, and do they actively elicit information to resolve such knowledge gaps? In a cross-situational word learning task, we asked 5-year-olds, 6- to 9-year-olds and adults to estimate their knowledge of newly learned word-object associations. We then examined whether participants preferentially sampled objects they reported not knowing the label in order to hear their labels again. We also examined whether such uncertainty-driven sampling behaviour led to improved learning. We found that all age groups were sensitive to gaps in their knowledge of the word-object associations, i.e. were more likely to say they had correctly indicated the label of an object when they were correct, relative to when they were incorrect. Furthermore, 6- to 9-year-olds and adults-but not 5-year-olds-were more likely to sample objects whose labels they reported not knowing. In other words, older children and adults displayed sampling behaviour directed at reducing knowledge gaps and uncertainty, while younger children did not. However, participants who displayed more uncertainty-driven sampling behaviour were not more accurate at test. Our findings underscore the role of uncertainty in driving 6- to 9-year-olds' and adults' sampling behaviour and speak to the mechanisms underlying previously reported performance boosts in active learning.

Spatial motifs reveal patterns in cellular architecture of complex tissues.

Spatial organization of cells is crucial to both proper physiological function of tissues and pathological conditions like cancer. Recent advances in spatial transcriptomics have enabled joint profiling of gene expression and spatial context of the cells. The outcome is an information rich map of the tissue where individual cells, or small regions, can be labeled based on their gene expression state. While spatial transcriptomics excels in its capacity to profile numerous genes within the same sample, most existing methods for analysis of spatial data only examine distribution of one or two labels at a time. These approaches overlook the potential for identifying higher-order associations between cell types - associations that can play a pivotal role in understanding development and function of complex tissues. In this context, we introduce a novel method for detecting motifs in spatial neighborhood graphs. Each motif represents a spatial arrangement of cell types that occurs in the tissue more frequently than expected by chance. To identify spatial motifs, we developed an algorithm for uniform sampling of paths from neighborhood graphs and combined it with a motif finding algorithm on graphs inspired by previous methods for finding motifs in DNA sequences. Using synthetic data with known ground truth, we show that our method can identify spatial motifs with high accuracy and sensitivity. Applied to spatial maps of mouse retinal bipolar cells and hypothalamic preoptic region, our method reveals previously unrecognized patterns in cell type arrangements. In some cases, cells within these spatial patterns differ in their gene expression from other cells of the same type, providing insights into the functional significance of the spatial motifs. These results suggest that our method can illuminate the substantial complexity of neural tissues, provide novel insight even in well studied models, and generate experimentally testable hypotheses.

I was like sh*t this is gonna hurt': Implementing Self-Sampling of Dried Blood Spots to Measure HIV Viral Load.

Background: Sexual minority men (SMM) with HIV who use stimulants may experience greater difficulties with antiretroviral therapy adherence which amplifies risk for unsuppressed HIV viral load (VL). Remote monitoring of VL could support efforts to rapidly respond to sub-optimal adherence. Methods: This qualitative study enrolled 24 SMM with HIV who use stimulants to examine experiences with two different dried blood spots (DBS) self-sampling devices (i.e., Tasso-M20 vs. HemaSpot HD) to measure VL. Participants were asked to complete self-sampling of DBS using both devices, and then participated in a 45-minute semi-structured interview. Interviews focused on ease of use, device preference, experiences with receiving and mailing kits, and barriers to participating in research. A thematic analysis was conducted to analyze interviews transcripts. Results: Twenty-two participants (92%) returned the Tasso-M20 and 21 (88%) returned the Hemaspot HD devices. Among the 22 participants that completed qualitative interviews, twenty-three codes were identified and collapsed within seven themes. Preferences for devices were based on convenience, pain and prior experiences with finger-pricking technology. Participants emphasized that clearer instructions with contingency plans for self-sampling of DBS would improve the user experience with self-sampling of DBS. Intersectional stigma (e.g., HIV, sexual minority status, and substance use) was noted as an important consideration in implementing self-sampling of DBS. Promoting decision making, or the option to choose sampling method based on personal preferences, may improve engagement and likelihood of DBS completion. Conclusions: Findings will guide the broader implementation of self-sampling of DBS to optimize VL monitoring in SMM with HIV who use stimulants.

Enhanced Inference for Finite Population Sampling-Based Prevalence Estimation with Misclassification Errors.

Epidemiologic screening programs often make use of tests with small, but non-zero probabilities of misdiagnosis. In this article, we assume the target population is finite with a fixed number of true cases, and that we apply an imperfect test with known sensitivity and specificity to a sample of individuals from the population. In this setting, we propose an enhanced inferential approach for use in conjunction with sampling-based bias-corrected prevalence estimation. While ignoring the finite nature of the population can yield markedly conservative estimates, direct application of a standard finite population correction (FPC) conversely leads to underestimation of variance. We uncover a way to leverage the typical FPC indirectly toward valid statistical inference. In particular, we derive a readily estimable extra variance component induced by misclassification in this specific but arguably common diagnostic testing scenario. Our approach yields a standard error estimate that properly captures the sampling variability of the usual bias-corrected maximum likelihood estimator of disease prevalence. Finally, we develop an adapted Bayesian credible interval for the true prevalence that offers improved frequentist properties (i.e., coverage and width) relative to a Wald-type confidence interval. We report the simulation results to demonstrate the enhanced performance of the proposed inferential methods.

Unlocking the self: Can microdosing psychedelics make one feel more authentic?

Background and aim: In the present study, we focus on the relationship between state authenticity - the experience of being true to oneself in a particular moment - and microdosing - a practice that implies repeatedly ingesting very small doses of psychedelics that do not reach the threshold for perceptual alterations. We propose that microdosing could increase state authenticity through influencing people's mood and the number and satisfaction with daily activities. Methods: We used self-assessments of state authenticity collected from 18 microdosers in the Netherlands across the period of 1 month for a total of 192 observations. Results: We found that on the microdosing day and the day thereafter, state authenticity was significantly higher. Furthermore, the number of activities and the satisfaction with them were higher on the day when participants microdosed, while the following day only the number of activities was higher. Both the number or activities and the satisfaction with them were positively related to state authenticity. Conclusion: We propose that feeling and behaving authentically could have a central role in explaining the positive effects of microdosing on health and wellbeing that are reported by current research.

A Case of MRI-Negative Leptomeningeal Disease From Non-small Cell Lung Cancer.

Leptomeningeal disease (LMD) is a rare complication of advanced non-small cell lung cancer (NSCLC), associated with a poor prognosis. We report the case of a 55-year-old man, who presented with a metastatic NSCLC with limited brain and abdominal metastases. He was treated with both chemoimmunotherapy and stereotactic radiotherapy (SRT) to the brain. Despite treatment, the patient experienced progressive neurological symptoms not in keeping with the extent of disease seen on imaging of the brain. Due to this incongruence between symptoms and radiologic findings, he underwent a lumbar puncture, which had positive cytology for LMD. He had a rapid progression of symptoms and died six days after the discovery of LMD. We review the available literature regarding the prevalence of MRI-negative LMD from a solid primary malignancy.

Acceptability of self-collected vaginal samples for human papillomavirus testing for primary cervical cancer screening: comparison of face-to-face and online recruitment modes.

BACKGROUND: This study aimed to assess the acceptability and attitudes of women towards human papillomavirus (HPV) self-sampling and compare the effectiveness of two delivery modes utilising face-to-face and online website for cervical cancer screening in Hong Kong. METHODS: Women aged 30-65 years were invited to participate by distributing the study information pamphlets at the specialist clinics of a regional acute hospital. Those who were interested in participating were given the option to join directly face-to-face or through an online website. All participants provided informed consent and received self-sampling kits and acceptability questionnaires either immediately (face-to-face) or through the post after registering at the website (online). All participants were requested to collect their own vaginal samples using a swab which was then brushed on a DNA sample storage card and returned to the hospital either in person or by post. The self-collected samples were tested for high-risk HPV using the Sentis™ HPV assay, a validated isothermal nucleic acid amplification real-time fluorescent detection assay. The primary outcome was the uptake rate of HPV self-sampling. RESULTS: Of the 1998 women recruited (1200 face-to-face, 798 online), 1377 returned their samples, giving an overall uptake rate of 68.9%. The uptake rate was significantly greater in the face-to-face mode than in the online mode (74.6% vs. 60.4%, p < 0.001). The median age of the participants was 49 years, 43.7% were never or under-screened, and 7.1% had high-risk HPV detected. Overall, 82.1% of the participants reported self-sampling convenient, and 79.3% were not embarrassed when collecting self-samples. However, only 49.8% were confident that they had collected the self-samples correctly. Most (91.1%) of the participants expressed willingness to perform self-sampling again, mostly because it was simple (79.2%) and quick (56.3%). CONCLUSIONS: HPV self-sampling can serve as an alternative primary screening method for cervical cancer in Hong Kong, especially for individuals who have not been adequately screened in the past. Both face-to-face and online website recruitment were associated with high acceptability, emphasising the potential benefits of utilising different platforms and strategies for reaching diverse populations.

Bayesian Inference for High Dimensional Cox Models with Gaussian and Diffused-Gamma Priors: A Case Study of Mortality in COVID-19 Patients Admitted to the ICU.

Bayesian approaches have been utilized to address the challenge of variable selection and statistical inference in high-dimensional survival analysis. However, the discontinuity of the ℓ0-norm prior, including the useful spike-and-slab prior, may lead to computational and implementation challenges, potentially limiting the widespread use of Bayesian methods. The Gaussian and diffused-gamma (GD) prior has emerged as a promising alternative due to its continuous-and-differentiable ℓ0-norm approximation and computational efficiency in generalized linear models. In this paper, we extend the GD prior to semi-parametric Cox models by proposing a rank-based Bayesian inference procedure with the Cox partial likelihood. We develop a computationally efficient algorithm based on the iterative conditional mode (ICM) and Markov chain Monte Carlo methods for posterior inference. Our simulations demonstrate the effectiveness of the proposed method, and we apply it to an electronic health record dataset to identify risk factors associated with COVID-19 mortality in ICU patients at a regional medical center.

Variation in Pen-Level Prevalence of BRD Bacterial Pathogens and Antimicrobial Resistance Following Feedlot Arrival in Beef Calves.

Antimicrobials are crucial for treating bovine respiratory disease (BRD) in beef feedlots. Evidence is needed to support antimicrobial use (AMU) decisions, particularly in the early part of the feeding period when BRD risk is highest. The study objective was to describe changes in prevalence and antimicrobial susceptibility of BRD bacterial pathogens at feedlot processing (1 day on feed (1DOF)), 12 days later (13DOF), and for a subset at 36DOF following metaphylactic antimicrobial treatment. Mixed-origin steer calves (n = 1599) from Western Canada were managed as 16 pens of 100 calves, receiving either tulathromycin (n = 1199) or oxytetracycline (n = 400) at arrival. Deep nasopharyngeal swabs collected at all time points underwent culture and antimicrobial susceptibility testing (AST). Variability in the pen-level prevalence of bacteria and antimicrobial susceptibility profiles were observed over time, between years, and metaphylaxis options. Susceptibility to most antimicrobials was high, but resistance increased from 1DOF to 13DOF, especially for tetracyclines and macrolides. Simulation results suggested that sampling 20 to 30 calves per pen of 200 reflected the relative pen-level prevalence of the culture and AST outcomes of interest. Pen-level assessment of antimicrobial resistance early in the feeding period can inform the evaluation of AMU protocols and surveillance efforts and support antimicrobial stewardship in animal agriculture.

Six Steps towards a Spatial Design for Large-Scale Pollinator Surveillance Monitoring.

Despite the importance of pollinators to ecosystem functioning and human food production, comprehensive pollinator monitoring data are still lacking across most regions of the world. Policy-makers have recently prioritised the development of large-scale monitoring programmes for pollinators to better understand how populations respond to land use, environmental change and restoration measures in the long term. Designing such a monitoring programme is challenging, partly because it requires both ecological knowledge and advanced knowledge in sampling design. This study aims to develop a conceptual framework to facilitate the spatial sampling design of large-scale surveillance monitoring. The system is designed to detect changes in pollinator species abundances and richness, focusing on temperate agroecosystems. The sampling design needs to be scientifically robust to address questions of agri-environmental policy at the scales of interest. To this end, we followed a six-step procedure as follows: (1) defining the spatial sampling units, (2) defining and delimiting the monitoring area, (3) deciding on the general sampling strategy, (4) determining the sample size, (5) specifying the sampling units per sampling interval, and (6) specifying the pollinator survey plots within each sampling unit. As a case study, we apply this framework to the "Wild bee monitoring in agricultural landscapes of Germany" programme. We suggest this six-step procedure as a conceptual guideline for the spatial sampling design of future large-scale pollinator monitoring initiatives.

MinION Sequencing of Fungi in Sub-Saharan African Air and a Novel LAMP Assay for Rapid Detection of the Tropical Phytopathogenic Genus Lasiodiplodia.

To date, there have been no DNA-based metabarcoding studies into airborne fungi in tropical Sub-Saharan Africa. In this initial study, 10 air samples were collected onto Vaseline-coated acrylic rods mounted on drones flown at heights of 15-50 meters above ground for 10-15 min at three sites in Ghana. Purified DNA was extracted from air samples, the internal transcribed spacer (ITS) region was amplified using fungal-specific primers, and MinION third-generation amplicon sequencing was undertaken with downstream bioinformatics analyses utilizing GAIA cloud-based software (at genus taxonomic level). Principal coordinate analyses based on Bray-Curtis beta diversity dissimilarity values found no clear evidence for the structuring of fungal air communities, nor were there significant differences in alpha diversity, based on geographic location (east vs. central Ghana), underlying vegetation type (cocoa vs. non-cocoa), or height above ground level (15-23 m vs. 25-50 m), and despite the short flight times (10-15 min), ~90 operational taxonomic units (OTUs) were identified in each sample. In Ghanaian air, fungal assemblages were skewed at the phylum taxonomic level towards the ascomycetes (53.7%) as opposed to basidiomycetes (24.6%); at the class level, the Dothideomycetes were predominant (29.8%) followed by the Agaricomycetes (21.8%). The most common fungal genus in Ghanaian air was cosmopolitan and globally ubiquitous Cladosporium (9.9% of reads). Interestingly, many fungal genera containing economically important phytopathogens of tropical crops were also identified in Ghanaian air, including Corynespora, Fusarium, and Lasiodiplodia. Consequently, a novel loop-mediated isothermal amplification (LAMP) assay, based on translation elongation factor-1α sequences, was developed and tested for rapid, sensitive, and specific detection of the fungal phytopathogenic genus Lasiodiplodia. Potential applications for improved tropical disease management are considered.

Sampling of microplastics at a materials recovery facility.

Detecting, separating, and characterizing airborne microplastics from other airborne particulates is currently challenging due to the various instrumental constraints and related sample preparation hurdles that must be overcome. The ability to measure these real-world environments is needed to better assess the risks associated with microplastics. To that end, the current study focused on developing a methodology for sampling and characterizing airborne microplastics. Particulate sampling was carried out at a municipal materials recovery facility near a conveyer belt containing sorted plastic materials to collect airborne environmental particles on filters. Nucleopore filters were mounted on Teflon support rings, coated with 100 nm aluminum to reduce the background signal for micro-Raman spectroscopy, and marked with a fiducial pattern using a laser engraver. The fiducial pattern was crucial in identifying samples, relocating particles, and efficiently enabling orthogonal measurements on the same samples. Optimum sampling conditions of 2 h at 25 L/min were determined using light microscopy to evaluate the particle loadings. The filters were then cut into slices which were attached to sections of thin beryllium-copper sheeting for easy transfer of the filter between microscopy platforms. Scanning electron microscopy was used to identify carbon-rich particles. Light microscopy was used to identify colored particles which were also carbon-rich which were then analyzed using micro-Raman spectroscopy to identify specific polymers.

Development of an analytical method for the determination of more than 300 pesticides and metabolites in the particulate and gaseous phase of ambient air.

Pesticides can enter the atmosphere during spraying or after application, resulting in environmental or human exposure. The study describes the optimisation and validation of analytical methods for the determination of more than 300 pesticides in the particulate and gaseous phases of the air. Pesticides were sampled with high-volume air samplers on glass-fibre filters (GFFs) and glass columns filled with polyurethane foam (PUF) and XAD-2 resin. Comparing different extraction methods, a QuEChERS extraction with acetonitrile was selected for the GFFs. For the PUF/XAD-2 columns, a cold-column extraction with dichloromethane was used. Instrumental determination was performed using liquid chromatography/electrospray ionisation-time-of-flight mass spectrometry (LC/ESI-QTOF) and gas chromatography/electron impact ionisation-tandem mass spectrometry (GC/EI-MS/MS). Recovery experiments showed recovery rates between 70 and 120% for 263 compounds on the GFFs and 75 compounds on the PUF/XAD-2 columns. Semi-quantitative determination was performed for 39 compounds on the GFFs and 110 compounds on the PUF/XAD-2 columns. Finally, 27 compounds on the GFFs and 138 compounds on the PUF/XAD-2 columns could be determined only qualitatively. For the determination of the PUF/XAD-2 samples, signal suppression (LC) or signal enhancement (GC) due to matrix effects were determined. Method quantification limits of the optimised methods ranged from 30 to 240 pg/m3 for the target compounds on the GFFs, and from 8 to 60 pg/m3 on the PUF/XAD-2 columns. The applicability of the method was demonstrated by means of environmental air samples from an agricultural area in the Netherlands.