Selection of key ambient particulate variables for epidemiological studies - applying cluster and heatmap analyses as tools for data reduction

Reliability and validity of facial expression recognition in the screening of generalized anxiety disorder

INTRODUCTION

Anxiety disorder is one of the most prevalent mental disorders in China. However, there are obvious subjective factors in the current assessment of anxiety disorders, which may lead to certain diagnostic errors. The identification and diagnosis of anxiety disorders can be further improved if objective biological indicators are added in the assessment process. The current research validates facial expression recognition as a screening tool to assist in detecting generalized anxiety disorder.

METHODS

Based on the International Affective Picture System, we constructed an aided diagnostic experimental paradigm and recorded their facial expression. The split-half reliability was displayed by the Pearson correlation heatmap. The paradigm, GAD-7 and HAMA scales were administered to 60 generalized anxiety disorder patients and 60 matched healthy controls to evaluate the criterion-related validity. Additionally, we conducted a diagnostic study by using MINI as a gold standard and calculated ROC analysis to examine the screening performance of the facial expressions.

RESULTS

The heatmap showed very high correlations (r > 0.60, PS < 0.05) along the diagonal of the square heatmap (from the bottom left corner to the top right). The Pearson correlation coefficients between the GAD-7, HAMA and seven facial expressions ranged from -0.35(neutral, P < 0.01) to 0.34(angry, P < 0.01). The intergroup effects of neutral, anger and fear emotions were statistically significant (F = 18.893, P < 0.001; F = 20.535, P < 0.001; F = 9.091, P = 0.003). ROC analysis showed AUC for neutral, angry and scared facial expressions were 0.723, 0.792 and 0.727 respectively.

CONCLUSION

This study constructed a tool for auxiliary screening of GAD patients and provided an objective automatic facial expression recognition method to assist psychological diagnosis.

Characteristic changes and environmental indicators of magnetic spherules in the South Yellow Sea mud area for about 7.5 ka

Magnetic spherules originate from anthropogenic and natural sources and can be differentiated based on morphology and composition. Using magnetic measurement, diameter measurement, scanning electron microscopy and energy dispersive spectroscopy (SEM-EDS) analysis of a 10 m sediment core in the mud area of the South Yellow Sea, we found that magnetic spherules occur at all observed depths of the core. The magnetic spherule concentrations vary from 10 spherules/0.5 g to 62 spherules/0.5 g. Here, concentrations generally less than 10 spherules/0.5 g are considered as the background value in the core. The peak value of magnetic spherules appeared at the 0.02, 0.3, 2 and 8 m, and their concentrations are 62, 52, 36, 48 spherules/0.5 g, respectively. According to the deposition age, concentration, diameter, morphology and chemical characteristics of the spherules, it is found that the spherules at 0.02 m are produced by industrial coal burning. A volcanic eruption event was the main responsible for the accumulation of spherules at 0.3 and 8 m, while the spherules located at 2 m are related to a wildfire event. Magnetic spherules are common in continental shelf regions and can well document the human activities and natural environment events.

Real-time authentication of minced shrimp by rapid evaporative ionization mass spectrometry

Minced shrimp is popular seafood due to its delicious flavor and nutritional value. However, the biological species of raw material of minced shrimp are not distinguished by naked eyes after processing. Thus, an in situ and real-time minced shrimp authentication method was established using iKnife rapid evaporative ionization mass spectrometry (REIMS) based lipidomics. The samples were analyzed under ambient ionization without any tedious preparation step. Seven economic shrimp samples were tested, whose phenotypes were used to develop a real-time recognition model. A total of 19 fatty acids and 45 phospholipid molecular species were efficiently identified and statistically analyzed by multivariate statistical analysis. The results showed that the seven shrimp species were well distinguished, and the most contributing ions at m/z 255.2, 279.2, 301.2, 327.2, 699.5, 742.5, etc., were revealed by variable importance in projection. The proposed iKnife REIMS showed excellent performance in minced shrimp authentication.

Associations of ultrafine and fine particles with childhood emergency room visits for respiratory diseases in a megacity

BACKGROUND

Ambient fine particulate matter with aerodynamic diameter less than 2.5 µm (PM_2.5) has been associated with deteriorated respiratory health, but evidence on particles in smaller sizes and childhood respiratory health has been limited.

METHODS

We collected time-series data on daily respiratory emergency room visits (ERVs) among children under 14 years old in Beijing, China, during 2015-2017. Concurrently, size-fractioned number concentrations of particles in size ranges of 5-560 nm (PNC_5-560) and mass concentrations of PM_2.5, black carbon (BC) and nitrogen dioxide (NO₂) were measured from a fixed-location monitoring station in the urban area of Beijing. Confounder-adjusted Poisson regression models were used to estimate excessive risks (ERs) of particle size fractions on childhood respiratory ERVs, and positive matrix factorisation models were applied to apportion the sources of PNC_5-560.

RESULTS

Among the 136 925 cases of all-respiratory ERVs, increased risks were associated with IQR increases in PNC_25-100 (ER=5.4%, 95% CI 2.4% to 8.6%), PNC_100-560 (4.9%, 95% CI 2.5% to 7.3%) and PM_2.5 (1.3%, 95% CI 0.1% to 2.5%) at current and 1 prior days (lag0-1). Major sources of PNC_5-560 were identified, including nucleation (36.5%), gasoline vehicle emissions (27.9%), diesel vehicle emissions (18.9%) and secondary aerosols (10.6%). Emissions from gasoline and diesel vehicles were found of significant associations with all-respiratory ERVs, with increased ERs of 6.0% (95% CI 2.5% to 9.7%) and 4.4% (95% CI 1.7% to 7.1%) at lag0-1 days, respectively. Exposures to other traffic-related pollutants (BC and NO₂) were also associated with increased respiratory ERVs.

CONCLUSION

Our findings suggest that exposures to higher levels of PNC_5-560 from traffic emissions could be attributed to increased childhood respiratory morbidity, which supports traffic emission control priority in urban areas.

Efficacy of multi-walled carbon nanotubes in regulating growth performance, total glutathione and redox state of Calendula officinalis L. cultivated on Pb and Cd polluted soil

Multi-walled carbon nanotubes (MWCNTs) have recently attracted huge attention to their impacts on the environment and plants. Therefore, this experiment was conducted to investigate the responses of lead (Pb) and cadmium (Cd) exposed pot marigold plants to various levels of MWCNT. Calendula officinalis (L.) seedlings were cultivated in Pb and Cd-polluted soils with exposure to 0, 50, 100, 250, 500 and 1000 mg L^-1 of MWCNT. The results demonstrated that foliar-applied MWCNT up to 250 mg L^-1 not only alleviated Pb and Cd-induced toxicity by reducing oxidative damage and boosting both enzymatic and non-enzymatic antioxidant defense system but also promoted the phytoremediation property of pot marigold plants by enhancing the accumulation of both Pb and Cd from the soil. Interestingly, oxidative damage exacerbation and both Pb and Cd accumulation reduction were noticed in pot marigold seedlings exposed to 500 and 1000 mg L^-1 MWCNTs. The findings of this study clearly showed that the use of appropriate concentrations of MWCNTs in increasing the phytoremediation properties of pot marigold was justified, while the use of high concentrations is toxic to the plant and intensifies the toxic effects of heavy metals (HMs) on plant physiology. This study provides a novel method to facilitate the phytoremediation of HMs polluted soils using MWCNT as well as explores the potential risks of these nanoparticles to the plants.

Impact of particulate matter on primary leaves of Vigna radiata (L.) R. Wilczek

Particulate matter (PM) pollution is of great concern for human health and vegetation. In this study, we investigated the impact of PM on primary (unifoliate) leaves of Vigna radiata (L.) R.Wilczek by exposing leaves' adaxial surface to PM. Leaves exposed to PM showed accumulation of various metal(loid)s even after removal of epicuticular wax (EW) revealing that the metals/metalloids could penetrate through the cuticular barrier. Scanning electron microscopic studies revealed that even after thorough washing with water, a significant amount (~55%) of particles were retained on the leaf surface. Leaves did not show any particles on their surface post EW removal, revealing that particles adhered to EW. Exposing primary leaves to PM did not alter their size but gave rise to smaller sized trifoliate leaves. A decline in Chl a/b of PM-exposed primary leaves suggested that PM cause a shading effect on leaves. PM-exposed primary leaves also showed a decline in sugar levels. However, the trifoliate leaves did not show any variation in Chl a/b as well as sugar levels. Our findings furnish evidence for the negative effects of PM on plants and a probable dietary exposure of humans to PMs, warranting more in-depth studies on the potential risks of PMs in agricultural sector.

Growth responses and physiological and biochemical changes in five ornamental plants grown in urban lead-contaminated soils

An increasing concentration of lead (Pb) in urban contaminated soil due to anthropogenic activities has been a global issue threatening human health. The use of urban ornamental plants as phytoremediation of Pb-contaminated soil is a new choice. In the present experiment, the physiological and biochemical response of five ornamental plants to increase in concentrations of C4H6O4Pb·H2O in the soil were measured to investigate these plans' Pb tolerance strategies and abilities. Our results showed that Pb stress significantly inhibited the growth and the biomass of all the plants. The root activity (RA), net photosynthetic rate (P n), and chlorophyll (Chl) content in Pb-stressed leaves were significantly decreased, whereas the leaf proline (Pro), soluble sugar (SS), and membrane stability index (MSI) were remarkable increased compared with those in the control group. By application of all-subsets regression and linear regression, the reduction in photosynthetic capacity in the five plants is mainly due to the decrease in the leaf Chl content caused by Pb stress. The bioconcentration factor (BCF) in Canna generalis was greater than 1, while in the other plants were lower than 1, suggesting that Canna generalis had the highest Pb accumulation ability. The translocation factor (TF) in all the plants were lower than 1, suggesting that Pb preferentially accumulated in the external part of roots. By calculating the comprehensive evaluation value (CEV), Iris germanica L. was found to be the most sensitive species, and Canna generalis was the most tolerant species, to Pb stress among the five ornamental plants.

Hourly Exposure to Ultrafine Particle Metrics and the Onset of Myocardial Infarction in Augsburg, Germany

BACKGROUND

Epidemiological evidence on the health effects of ultrafine particles (UFP) remains insufficient to infer a causal relationship that is largely due to different size ranges and exposure metrics examined across studies. Moreover, evidence regarding the association between UFP and cardiovascular disease at a sub-daily timescale is lacking.

OBJECTIVE

We investigated the relationship between different particle metrics, including particle number (PNC), length (PLC), and surface area (PSC) concentrations, and myocardial infarction (MI) at an hourly timescale.

METHODS

We collected hourly air pollution and meteorological data from fixed urban background monitoring sites and hourly nonfatal MI cases from a MI registry in Augsburg, Germany, during 2005-2015. We conducted a time-stratified case-crossover analysis with conditional logistic regression to estimate the association between hourly particle metrics and MI cases, adjusted for air temperature and relative humidity. We also examined the independent effects of a certain particle metric in two-pollutant models by adjusting for copollutants, including particulate matter (PM) with an aerodynamic diameter of ≤10μm or 2.5μm (PM10 and PM2.5, respectively), nitrogen dioxide, ozone, and black carbon.

RESULTS

Overall, a total of 5,898 cases of nonfatal MI cases were recorded. Exploratory analyses showed similar associations across particle metrics in the first 6-12 h. For example, interquartile range increases in PNC within the size range of 10-100 nm, PLC, and PSC were associated with an increase of MI 6 h later by 3.27% [95% confidence interval (CI): 0.27, 6.37], 5.71% (95% CI: 1.79, 9.77), and 5.84% (95% CI: 1.04, 10.87), respectively. Positive, albeit imprecise, associations were observed for PNC within the size range of 10-30 nm and 100-500 nm. Effect estimates for PLC and PSC remained similar after adjustment for PM and gaseous pollutants.

CONCLUSIONS

Transient exposure to particle number, length, and surface area concentrations or other potentially related exposures may trigger the onset of nonfatal myocardial infraction. https://doi.org/10.1289/EHP5478.

Dendrograms, heat maps and principal component analysis - the practical use of statistical methods for source apportionment of trace elements in PM10

Dendrogram (DE), heat map (HM) and principal component analysis (PCA) methods were used in order to identify possible emission sources of As, Cd, Co, Cr, Hg, Mn, Ni, Pb, Sb and Se in ambient PM10 collected in the surroundings of working power plants. Each statistical tool resulted in slightly different clusters. The best approximation of possible emission sources was received by the use of statistical analysis of trace-element concentrations combined with characterization of the sampling sites. In the study, PCA was indicated as the most useful statistical tool for source apportionment of trace elements in PM10. Major sources identified by PCA included: (1) coal combustion, (2) soil and road-dust resuspension, (3) the use of pesticides and (4) waste incineration.

Recognition of Lung Adenocarcinoma-specific Gene Pairs Based on Genetic Algorithm and Establishment of a Deep Learning Prediction Model

AIM AND OBJECTIVE

Lung cancer is a disease with a dismal prognosis and is the major cause of cancer deaths in many countries. Nonetheless, rapid technological developments in genome science guarantees more effective prevention and treatment strategies.

MATERIALS AND METHODS

In this study, genes were pair-matched and screened for lung adenocarcinomaspecific gene relationships. False positives due to fluctuations in single gene expression were avoided and the stability and accuracy of the results was improved.

RESULTS

Finally, a deep learning model was constructed with machine learning algorithm to realize the clinical diagnosis of lung adenocarcinoma in patients.

CONCLUSION

Comparing with the traditional methods which takes ingle gene as a feature, the relative difference between gene pairs is a higher order feature, leverage high-order features to build the model can avoid instability caused by a single gene mutation, making the prediction results more reliable.

Prediction of Lymph Node Metastasis in Patients With Papillary Thyroid Carcinoma: A Radiomics Method Based on Preoperative Ultrasound Images

BACKGROUND

Papillary thyroid carcinoma is a type of indolent tumor with a dramatically increasing incidence rate and stably high survival rate. Reducing the overdiagnosis and overtreatment of papillary thyroid carcinoma is clinically emergent and important. A radiomics model is proposed in this article to predict lymph node metastasis, the most important risk factor of papillary thyroid carcinoma, based on noninvasive routine preoperative ultrasound images.

METHODS

Four hundred fifty ultrasound manually segmented images of patients with papillary thyroid carcinoma with lymph node status obtained from pathology report were enrolled in our retrospective study. A radiomics evaluation of 614 high-throughput features were calculated, including size, shape, margin, boundary, orientation, position, echo pattern, posterior acoustic pattern, and calcification features. Then, combined feature selection strategy was used to select features with the greatest ability to discriminate lymph node status. A support vector machine classifier was employed to build and validate the prediction model. Another independent testing cohort was used to further evaluate the performance of the radiomics model.

RESULTS

Among 614 radiomics features, 50 selected features most reflecting echo pattern, posterior acoustic pattern, and calcification showed the superior lymph node status distinguishable performance with area under the receiver operating characteristic curve of 0.753, 0.740, and 0.743 separately when using each type of features predicting the lymph node status. The results of model based on all 50 final features predicting the lymph node status shown an area under the receiver operating characteristic curve of 0.782, and accuracy of 0.712. In the independent testing cohort, the proposed approach showed similar results, with area under the receiver operating characteristic curve of 0.727 and accuracy of 0.710.

CONCLUSION

Papillary thyroid carcinoma with lymph node metastasis usually shows a complex echo pattern, posterior region homogeneity, and macrocalcification or multiple calcification. The radiomics model proposed in this article is a promising method for assessing the risk of papillary thyroid carcinoma metastasis noninvasively.

Identification of esophageal cancer pathway deviation and construction of a diagnosis model using three kernel genes

The purpose of this study is to better understand the role of interleukin 35 (IL35) in esophageal carcinoma by comparing the mRNA level in Barrett's esophageal mucosa and in matched normal squamous mucosa and to understand how the diagnosis model works with two other genes: hepatocyte nuclear factor 1B (HNF1B) and cAMP responsive element binding protein 3-like 1 (CREB3L1). By comparing carcinoma tissue and normal tissue samples, we extracted all the differentially expressed mRNAs. The bioinformatics analysis resulted in the discovery of three prominent genes. Eventually, the three genes were utilized to train a deep-learning model. An additional wet experiment was conducted to validate the effect of IL35. All the differentially expressed genes were enriched into nine groups, each of which has specific biological functions. Given that the three significant genes HNF1B, CREB3L1, and IL35 as diagnostic features, a deep-learning model was constructed, reaching an accuracy of 93% in the training set and 87% in the test set. Our findings suggest that IL35, along with the other two signatures, can distinguish esophageal tumor samples from normal samples precisely.

Comparison of the application of B-mode and strain elastography ultrasound in the estimation of lymph node metastasis of papillary thyroid carcinoma based on a radiomics approach

PURPOSE

B-mode ultrasound (B-US) and strain elastography ultrasound (SE-US) images have a potential to distinguish thyroid tumor with different lymph node (LN) status. The purpose of our study is to investigate whether the application of multi-modality images including B-US and SE-US can improve the discriminability of thyroid tumor with LN metastasis based on a radiomics approach.

METHODS

Ultrasound (US) images including B-US and SE-US images of 75 papillary thyroid carcinoma (PTC) cases were retrospectively collected. A radiomics approach was developed in this study to estimate LNs status of PTC patients. The approach included image segmentation, quantitative feature extraction, feature selection and classification. Three feature sets were extracted from B-US, SE-US, and multi-modality containing B-US and SE-US. They were used to evaluate the contribution of different modalities. A total of 684 radiomics features have been extracted in our study. We used sparse representation coefficient-based feature selection method with 10-bootstrap to reduce the dimension of feature sets. Support vector machine with leave-one-out cross-validation was used to build the model for estimating LN status.

RESULTS

Using features extracted from both B-US and SE-US, the radiomics-based model produced an area under the receiver operating characteristic curve (AUC) [Formula: see text] 0.90, accuracy (ACC) [Formula: see text] 0.85, sensitivity (SENS) [Formula: see text] 0.77 and specificity (SPEC) [Formula: see text] 0.88, which was better than using features extracted from B-US or SE-US separately.

CONCLUSIONS

Multi-modality images provided more information in radiomics study. Combining use of B-US and SE-US could improve the LN metastasis estimation accuracy for PTC patients.

Gene expression profiles reveal key genes for early diagnosis and treatment of adamantinomatous craniopharyngioma

Adamantinomatous craniopharyngioma (ACP) is an aggressive brain tumor that occurs predominantly in the pediatric population. Conventional diagnosis method and standard therapy cannot treat ACPs effectively. In this paper, we aimed to identify key genes for ACP early diagnosis and treatment. Datasets GSE94349 and GSE68015 were obtained from Gene Expression Omnibus database. Consensus clustering was applied to discover the gene clusters in the expression data of GSE94349 and functional enrichment analysis was performed on gene set in each cluster. The protein-protein interaction (PPI) network was built by the Search Tool for the Retrieval of Interacting Genes, and hubs were selected. Support vector machine (SVM) model was built based on the signature genes identified from enrichment analysis and PPI network. Dataset GSE94349 was used for training and testing, and GSE68015 was used for validation. Besides, RT-qPCR analysis was performed to analyze the expression of signature genes in ACP samples compared with normal controls. Seven gene clusters were discovered in the differentially expressed genes identified from GSE94349 dataset. Enrichment analysis of each cluster identified 25 pathways that highly associated with ACP. PPI network was built and 46 hubs were determined. Twenty-five pathway-related genes that overlapped with the hubs in PPI network were used as signatures to establish the SVM diagnosis model for ACP. The prediction accuracy of SVM model for training, testing, and validation data were 94, 85, and 74%, respectively. The expression of CDH1, CCL2, ITGA2, COL8A1, COL6A2, and COL6A3 were significantly upregulated in ACP tumor samples, while CAMK2A, RIMS1, NEFL, SYT1, and STX1A were significantly downregulated, which were consistent with the differentially expressed gene analysis. SVM model is a promising classification tool for screening and early diagnosis of ACP. The ACP-related pathways and signature genes will advance our knowledge of ACP pathogenesis and benefit the therapy improvement.

[What potential do geographic information systems have for population-wide health monitoring in Germany? : Perspectives and challenges for the health monitoring of the Robert Koch Institute]

Geographic information systems (GISs) are computer-based systems with which geographical data can be recorded, stored, managed, analyzed, visualized and provided. In recent years, they have become an integral part of public health research. They offer a broad range of analysis tools, which enable innovative solutions for health-related research questions. An analysis of nationwide studies that applied geographic information systems underlines the potential this instrument bears for health monitoring in Germany. Geographic information systems provide up-to-date mapping and visualization options to be used for national health monitoring at the Robert Koch Institute (RKI). Furthermore, objective information on the residential environment as an influencing factor on population health and on health behavior can be gathered and linked to RKI survey data at different geographic scales. Besides using physical information, such as climate, vegetation or land use, as well as information on the built environment, the instrument can link socioeconomic and sociodemographic data as well as information on health care and environmental stress to the survey data and integrate them into concepts for analyses. Therefore, geographic information systems expand the potential of the RKI to present nationwide, representative and meaningful health-monitoring results. In doing so, data protection regulations must always be followed. To conclude, the development of a national spatial data infrastructure and the identification of important data sources can prospectively improve access to high quality data sets that are relevant for the health monitoring.

Association of novel metrics of particulate matter with vascular markers of inflammation and coagulation in susceptible populations -results from a panel study

BACKGROUND AND AIMS

Epidemiological studies have shown adverse effects of ambient air pollutants on health with inflammation and oxidative stress playing an important role. We examine the association between blood biomarkers of inflammation and coagulation and physical attributes of particulate matter which are not routinely measured such as particle length or surface area concentration and apparent density of PM.

METHODS

Between 3/2007 and 12/2008 187 non-smoking individuals with type 2 diabetes mellitus (T2D) or impaired glucose tolerance (IGT) were examined within the framework of the KORA Study in Augsburg, Germany. In addition, we selected 87 participants with a potential genetic predisposition on detoxifying and inflammatory pathways. This was defined by the null polymorphism for glutathione S-transferase M1 in combination with a certain single nucleotide polymorphism on the C-reactive protein (CRP) gene (rs1205) or the fibrinogen gene (rs1800790). Participants had blood drawn up to seven different times, resulting in 1765 blood samples. Air pollutants were collected at a central measurement station and individual 24-h averages calculated. Associations between air pollutants and high sensitivity CRP, myeloperoxidase (MPO), interleukin (IL)-6 and fibrinogen were analysed using additive mixed models.

RESULTS

For the panel with genetic susceptibility, increases were seen for CRP and MPO with most attributes, specifically particle length and active surface concentration. The %change of geometric mean and 95% confidence intervals for the 5-day average exposure for CRP and MPO were 34.6% [21.8;48.8] and 8.3% [3.2;13.6] per interquartile range increase of particle length concentration and 29.8% [15.9;45.3] and 10.4 [4.4;16.7] for active surface area. Results for the panel of T2D and IGT and the other blood biomarkers were less conclusive.

CONCLUSIONS

Particle length concentration and active surface concentration showed strong positive associations with blood biomarkers reflecting inflammation. These air pollution metrics might reflect harmful aerosol properties better than particulate mass or number concentration. They might therefore be important for epidemiological studies.

Multi-pollutant exposure profiles associated with term low birth weight in Los Angeles County

Research indicates that multiple outdoor air pollutants and adverse neighborhood conditions are spatially correlated. Yet health risks associated with concurrent exposure to air pollution mixtures and clustered neighborhood factors remain underexplored. Statistical models to assess the health effects from pollutant mixtures remain limited, due to problems of collinearity between pollutants and area-level covariates, and increases in covariate dimensionality. Here we identify pollutant exposure profiles and neighborhood contextual profiles within Los Angeles (LA) County. We then relate these profiles with term low birth weight (TLBW). We used land use regression to estimate NO2, NO, and PM2.5 concentrations averaged over census block groups to generate pollutant exposure profile clusters and census block group-level contextual profile clusters, using a Bayesian profile regression method. Pollutant profile cluster risk estimation was implemented using a multilevel hierarchical model, adjusting for individual-level covariates, contextual profile cluster random effects, and modeling of spatially structured and unstructured residual error. Our analysis found 13 clusters of pollutant exposure profiles. Correlations between study pollutants varied widely across the 13 pollutant clusters. Pollutant clusters with elevated NO2, NO, and PM2.5 concentrations exhibited increased log odds of TLBW, and those with low PM2.5, NO2, and NO concentrations showed lower log odds of TLBW. The spatial patterning of pollutant cluster effects on TLBW, combined with between-pollutant correlations within pollutant clusters, imply that traffic-related primary pollutants influence pollutant cluster TLBW risks. Furthermore, contextual clusters with the greatest log odds of TLBW had more adverse neighborhood socioeconomic, demographic, and housing conditions. Our data indicate that, while the spatial patterning of high-risk multiple pollutant clusters largely overlaps with adverse contextual neighborhood cluster, both contribute to TLBW while controlling for the other.

Temporal Patterns of Larval Fish Occurrence in a Large Subtropical River

Knowledge of temporal patterns of larval fish occurrence is limited in south China, despite its ecological importance. This research examines the annual and seasonal patterns of fish larval presence in the large subtropical Pearl River. Data is based on samples collected every two days, from 2006 to 2013. In total, 45 taxa representing 13 families and eight orders were sampled. The dominant larval family was Cyprinidae, accounting for 27 taxa. Squaliobarbus curriculus was the most abundant species, followed by Megalobrama terminalis, Xenocypris davidi, Cirrhinus molitorella, Hemiculter leuscisculus and Squalidus argentatus. Fish larvae abundances varied significantly throughout the seasons (multivariate analyses: Cluster, SIMPROF and ANOSIM). The greatest numbers occurred between May and September, peaking from June through August, which corresponds to the reproductive season. In this study, redundancy analysis was used to describe the relationship between fish larval abundance and associated environmental factors. Mean water temperature, river discharge, atmospheric pressure, maximum temperature and precipitation play important roles in larval occurrence patterns. According to seasonal variations, fish larvae occurrence is mainly affected by water temperature. It was also noted that the occurrence of Salanx reevesii and Cyprinus carpio larvae is associated with higher dissolved oxygen (DO) concentrations, higher atmospheric pressure and lower water temperatures which occur in the spring. On the other hand, M. terminalis, X. davidi, and C. molitorella are associated with high precipitation, high river discharge, low atmospheric pressure and low DO concentrations which featured during the summer months. S. curriculus also peaks in the summer and is associated with peak water temperatures and minimum NH3-N concentrations. Rhinogobius giurinus occur when higher atmospheric pressure, lower precipitation and lower river discharges occur in the autumn. Dominant fish species stagger their spawning period to avoid intraspecific competition for food resources during early life stages; a coexistence strategy to some extent. This research outlines the environmental requirements for successful spawning for different fish species. Understanding processes such as those outlined in this research paper is the basis of conservation of fish community diversity which is a critical resource to a successful sustainable fishery in the Pearl River.

Assessing responses of cardiovascular mortality to particulate matter air pollution for pre-, during- and post-2008 Olympics periods

BACKGROUND

The link between particulate air pollution and cardiovascular (CVD) mortality has been investigated. However, there is little direct evidence that reduction measures which decrease particulate air pollution would lead to a reduction in CVD mortality.

OBJECTIVES

In Beijing, China, air quality improvement strategies were developed and actions were taken before and during the 2008 Olympic and Paralympic Games. Taking advantage of this opportunity, the aim of the study was to assess the effects of changes in particulate air pollution before (May 20-July 20, 2008), during (August 1-September 20, 2008) and after (October 1-December 1, 2008) the Olympics period.

METHODS

Concentrations of air pollution, meteorology and CVD death counts were obtained from official networks and monitoring sites located on the Peking University campus. Air pollution effects with lags of 0-4 days as well as of the 5-day average on cause-specific CVD mortality were investigated for the complete study period (May 20-December 1, 2008) using Quasi-Poisson regression models. Different gender and age subgroups were taken into account. Additionally, effect modification by air mass origin was investigated. In a second step, air pollution effects were estimated for the three specific periods by including an interaction term in the models.

RESULTS

We observed large concentration decreases in all measured air pollutants during the unique pollution intervention for the Beijing 2008 Olympics. For the whole period, adverse effects of particulate air pollution were observed on CVD mortality with a 1-day delay as well as for the 5-day average exposure, e.g. an 8.8% (95%CI: 2.7-15.2%) increase in CVD mortality with an interquartile range increase in ultrafine particles. The effects were more pronounced in females, the elderly and for cerebrovascular deaths, but not modified by air mass origin. The specific sub-period analysis results suggested that the risks of CVD mortality were lowest during the Olympic Games where strongest reduction measures have been applied.

CONCLUSIONS

The results indicated that the reduction of air pollution due to air quality control measures led to a decreased risk of CVD mortality in Beijing. Our findings provide new insight into efforts to reduce ambient air pollution.

An in vitro alveolar macrophage assay for the assessment of inflammatory cytokine expression induced by atmospheric particulate matter

Exposures to air pollution in the form of particulate matter (PM) can result in excess production of reactive oxygen species (ROS) in the respiratory system, potentially causing both localized cellular injury and triggering a systemic inflammatory response. PM-induced inflammation in the lung is modulated in large part by alveolar macrophages and their biochemical signaling, including production of inflammatory cytokines, the primary mechanism via which inflammation is initiated and sustained. We developed a robust, relevant, and flexible method employing a rat alveolar macrophage cell line (NR8383) which can be applied to routine samples of PM from air quality monitoring sites to gain insight into the drivers of PM toxicity that lead to oxidative stress and inflammation. Method performance was characterized using extracts of ambient and vehicular engine exhaust PM samples. Our results indicate that the reproducibility and the sensitivity of the method are satisfactory and comparisons between PM samples can be made with good precision. The average relative percent difference for all genes detected during 10 different exposures was 17.1%. Our analysis demonstrated that 71% of genes had an average signal to noise ratio (SNR) ≥ 3. Our time course study suggests that 4 h may be an optimal in vitro exposure time for observing short-term effects of PM and capturing the initial steps of inflammatory signaling. The 4 h exposure resulted in the detection of 57 genes (out of 84 total), of which 86% had altered expression. Similarities and conserved gene signaling regulation among the PM samples were demonstrated through hierarchical clustering and other analyses. Overlying the core congruent patterns were differentially regulated genes that resulted in distinct sample-specific gene expression "fingerprints." Consistent upregulation of Il1f5 and downregulation of Ccr7 was observed across all samples, while TNFα was upregulated in half of the samples and downregulated in the other half. Overall, this PM-induced cytokine expression assay could be effectively integrated into health studies and air quality monitoring programs to better understand relationships between specific PM components, oxidative stress activity and inflammatory signaling potential.

Analysing the health effects of simultaneous exposure to physical and chemical properties of airborne particles

BACKGROUND

Airborne particles are a complex mix of organic and inorganic compounds, with a range of physical and chemical properties. Estimation of how simultaneous exposure to air particles affects the risk of adverse health response represents a challenge for scientific research and air quality management. In this paper, we present a Bayesian approach that can tackle this problem within the framework of time series analysis.

METHODS

We used Dirichlet process mixture models to cluster time points with similar multipollutant and response profiles, while adjusting for seasonal cycles, trends and temporal components. Inference was carried out via Markov Chain Monte Carlo methods. We illustrated our approach using daily data of a range of particle metrics and respiratory mortality for London (UK) 2002-2005. To better quantify the average health impact of these particles, we measured the same set of metrics in 2012, and we computed and compared the posterior predictive distributions of mortality under the exposure scenario in 2012 vs 2005.

RESULTS

The model resulted in a partition of the days into three clusters. We found a relative risk of 1.02 (95% credible intervals (CI): 1.00, 1.04) for respiratory mortality associated with days characterised by high posterior estimates of non-primary particles, especially nitrate and sulphate. We found a consistent reduction in the airborne particles in 2012 vs 2005 and the analysis of the posterior predictive distributions of respiratory mortality suggested an average annual decrease of -3.5% (95% CI: -0.12%, -5.74%).

CONCLUSIONS

We proposed an effective approach that enabled the better understanding of hidden structures in multipollutant health effects within time series analysis. It allowed the identification of exposure metrics associated with respiratory mortality and provided a tool to assess the changes in health effects from various policies to control the ambient particle matter mixtures.

The gene expression profile of resistant and susceptible Bombyx mori strains reveals cypovirus-associated variations in host gene transcript levels

High-throughput paired-end RNA sequencing (RNA-Seq) was performed to investigate the gene expression profile of a susceptible Bombyx mori strain, Lan5, and a resistant B. mori strain, Ou17, which were both orally infected with B. mori cypovirus (BmCPV) in the midgut. There were 330 and 218 up-regulated genes, while there were 147 and 260 down-regulated genes in the Lan5 and Ou17 strains, respectively. Gene ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment for differentially expressed genes (DEGs) were carried out. Moreover, gene interaction network (STRING) analyses were performed to analyze the relationships among the shared DEGs. Some of these genes were related and formed a large network, in which the genes for B. mori cuticular protein RR-2 motif 123 (BmCPR123) and the gene for B. mori DNA replication licensing factor Mcm2-like (BmMCM2) were key genes among the common up-regulated DEGs, whereas the gene for B. mori heat shock protein 20.1 (Bmhsp20.1) was the central gene among the shared down-regulated DEGs between Lan5 vs Lan5-CPV and Ou17 vs Ou17-CPV. These findings established a comprehensive database of genes that are differentially expressed in response to BmCPV infection between silkworm strains that differed in resistance to BmCPV and implied that these DEGs might be involved in B. mori immune responses against BmCPV infection.

Associations between ambient air pollution and blood markers of inflammation and coagulation/fibrinolysis in susceptible populations

The pathophysiological pathways linking particulate air pollution to cardiovascular disease are still not fully understood. We examined the association between ambient air pollutants and blood markers of inflammation and coagulation/fibrinolysis in three potentially susceptible populations. Three panels of non-smoking individuals were examined between 3/2007 and 12/2008: 1) with type 2 diabetes mellitus (T2D, n=83), 2) with impaired glucose tolerance (IGT, n=104), and 3) with a potential genetic predisposition which could affect detoxifying and inflammatory pathways (n=87) defined by the null polymorphism for glutathione S-transferase M1 (GSTM1) in combination with a certain single nucleotide polymorphism on the C-reactive protein (CRP) or the fibrinogen gene. Study participants had blood drawn up to seven times every four to six weeks. In total, 1765 blood samples were analysed for CRP, interleukin (IL)-6, soluble CD40 ligand (sCD40L), fibrinogen, myeloperoxidase (MPO), and plasminogen activator inhibitor-1 (PAI-1). Hourly mean values of particulate air pollutants, particle number concentrations in different size ranges and gaseous pollutants were collected at fixed monitoring sites and individual 24hour averages calculated. Associations between air pollutants and blood markers were analysed for each panel separately and taking the T2D panel and the IGT panel together, using additive mixed models adjusted for long-term time trend and meteorology. For the panel with potential genetic susceptibility, CRP and MPO increased for most lags, especially with the 5-day average exposure (% change of geometric mean and 95% confidence interval: 22.9% [12.0;34.7] for CRP and 5.0% [0.3;9.9] for MPO per interquartile range of PM2.5). Small positive associations were seen for fibrinogen while sCD40L, PAI-1 and IL-6 mostly decreased in association with air pollution concentrations. Except for positive associations for fibrinogen we did not see significant results with the two other panels. Participants with potential genetic susceptibility showed a clear association between inflammatory blood biomarkers and ambient air pollutants. Our results support the hypothesis that air pollution increases systemic inflammation especially in susceptible populations which may aggravate atherosclerotic diseases and induce multi-organ damage.

Evaluating the application of multipollutant exposure metrics in air pollution health studies

BACKGROUND

Health effects associated with air pollution are typically evaluated using a single pollutant approach, yet people are exposed to mixtures consisting of multiple pollutants that may have independent or combined effects on human health. Development of exposure metrics that represent the multipollutant environment is important to understand the impact of ambient air pollution on human health.

OBJECTIVES

We reviewed existing multipollutant exposure metrics to evaluate how they can be applied to understand associations between air pollution and health effects.

METHODS

We conducted a literature search using both targeted search terms and a relational search in Web of Science and PubMed in April and December 2013. We focused on exposure metrics that are constructed from ambient pollutant concentrations and can be broadly applied to evaluate air pollution health effects.

RESULTS

Multipollutant exposure metrics were identified in 57 eligible studies. Metrics reviewed can be categorized into broad pollutant grouping paradigms based on: 1) source emissions and atmospheric processes or 2) common health outcomes.

DISCUSSION

When comparing metrics, it is apparent that no universal exposure metric exists; each type of metric addresses different research questions and provides unique information on human health effects. Key limitations of these metrics include the balance between complexity and simplicity as well as the lack of an existing "gold standard" for multipollutant health effects and exposure.

CONCLUSIONS

Future work on characterizing multipollutant exposure error and joint effects will inform development of improved multipollutant metrics to advance air pollution health effects research and human health risk assessment.

Employing heat maps to mine associations in structured routine care data

OBJECTIVE

Mining the electronic medical record (EMR) has the potential to deliver new medical knowledge about causal effects, which are hidden in statistical associations between different patient attributes. It is our goal to detect such causal mechanisms within current research projects which include e.g. the detection of determinants of imminent ICU readmission. An iterative statistical approach to examine each set of considered attribute pairs delivers potential answers but is difficult to interpret. Therefore, we aimed to improve the interpretation of the resulting matrices by the use of heat maps. We propose strategies to adapt heat maps for the search for associations and causal effects within routine EMR data.

METHODS

Heat maps visualize tabulated metric datasets as grid-like choropleth maps, and thus present measures of association between numerous attribute pairs clearly arranged. Basic assumptions about plausible exposures and outcomes are used to allocate distinct attribute sets to both matrix dimensions. The image then avoids certain redundant graphical elements and provides a clearer picture of the supposed associations. Specific color schemes have been chosen to incorporate preexisting information about similarities between attributes. The use of measures of association as a clustering input has been taken as a trigger to apply transformations which ensure that distance metrics always assume finite values and treat positive and negative associations in the same way. To evaluate the general capability of the approach, we conducted analyses of simulated datasets and assessed diagnostic and procedural codes in a large routine care dataset.

RESULTS

Simulation results demonstrate that the proposed clustering procedure rearranges attributes similar to simulated statistical associations. Thus, heat maps are an excellent tool to indicate whether associations concern the same attributes or different ones, and whether affected attribute sets conform to any preexisting relationship between attributes. The dendrograms help in deciding if contiguous sequences of attributes effectively correspond to homogeneous attribute associations. The exemplary analysis of a routine care dataset revealed patterns of associations that follow plausible medical constellations for several diseases and the associated medical procedures and activities. Cases with breast cancer (ICD C50), for example, appeared to be associated with radiation therapy (8-52). In cross check, approximately 60 percent of the attribute pairs in this dataset showed a strong negative association, which can be explained by diseases treated in a medical specialty which routinely does not perform the respective procedures in these cases. The corresponding diagram clearly reflects these relationships in the shape of coherent subareas.

CONCLUSION

We could demonstrate that heat maps of measures of association are effective for the visualization of patterns in routine care EMRs. The adjustable method for the assignment of attributes to image dimensions permits a balance between the display of ample information and a favorable level of graphical complexity. The scope of the search can be adapted by the use of pre-existing assumptions about plausible effects to select exposure and outcome attributes. Thus, the proposed method promises to simplify the detection of undiscovered causal effects within routine EMR data.

Long-term variation of the levels, compositions and sources of size-resolved particulate matter in a megacity in China

To investigate the long-term trends and variations of the levels, compositions, size distribution and sources of particulate matter (PM), long-term monitoring campaigns of PM10 and PM2.5 were performed in a megacity in China (Chengdu) during the period from 2009 to 2011. The average concentration of PM10 was 172.01±89.80 μg/m(3) and that of PM2.5 was 103.15±59.83 μg/m(3), with an average PM2.5/PM10 of 0.60. Enrichments of the important species indicated that the fractions of crustal elements were higher in PM10 than those in PM2.5, while the abundance of organic carbon (OC) and secondary ions was enriched in the fine PM. Quantitative source apportionments of both PM10 and PM2.5 were performed by PMF. PM10 and PM2.5 in Chengdu were influenced by similar source categories, and their percentage contributions were in the same order: crustal dust was the highest contributor, followed by vehicular exhaust, secondary sulfate, secondary nitrate and cement dust. Crustal dust and cement dust contributed a higher percentage to PM10 than to PM2.5, while vehicular exhaust and secondary particles provided higher percentage contributions to PM2.5. In addition, PMF-HCA was performed to investigate the characteristics of the sources of the clustered samples, identifying three periods: crustal dust dominant-period, secondary sulfate dominant-period and comprehensive source influenced-period. Planting, reduction of precursors, and banning high-emission vehicles should be implemented to control crustal dust, secondary particles and vehicular exhaust in Chengdu. Furthermore, the size-resolved and the period-resolved control would be more effective.