An Approach for Plant Leaf Image Segmentation Based on YOLOV8 and the Improved DEEPLABV3.

Accurate plant leaf image segmentation provides an effective basis for automatic leaf area estimation, species identification, and plant disease and pest monitoring. In this paper, based on our previous publicly available leaf dataset, an approach that fuses YOLOv8 and improved DeepLabv3+ is proposed for precise image segmentation of individual leaves. First, the leaf object detection algorithm-based YOLOv8 was introduced to reduce the interference of backgrounds on the second stage leaf segmentation task. Then, an improved DeepLabv3+ leaf segmentation method was proposed to more efficiently capture bar leaves and slender petioles. Densely connected atrous spatial pyramid pooling (DenseASPP) was used to replace the ASPP module, and the strip pooling (SP) strategy was simultaneously inserted, which enabled the backbone network to effectively capture long distance dependencies. The experimental results show that our proposed method, which combines YOLOv8 and the improved DeepLabv3+, achieves a 90.8% mean intersection over the union (mIoU) value for leaf segmentation on our public leaf dataset. When compared with the fully convolutional neural network (FCN), lite-reduced atrous spatial pyramid pooling (LR-ASPP), pyramid scene parsing network (PSPnet), U-Net, DeepLabv3, and DeepLabv3+, the proposed method improves the mIoU of leaves by 8.2, 8.4, 3.7, 4.6, 4.4, and 2.5 percentage points, respectively. Experimental results show that the performance of our method is significantly improved compared with the classical segmentation methods. The proposed method can thus effectively support the development of smart agroforestry.

Comparison of Depth-Specific Prediction of Soil Properties: MIR vs. Vis-NIR Spectroscopy.

The prediction of soil properties at different depths is an important research topic for promoting the conservation of black soils and the development of precision agriculture. Mid-infrared spectroscopy (MIR, 2500-25000 nm) has shown great potential in predicting soil properties. This study aimed to explore the ability of MIR to predict soil organic matter (OM) and total nitrogen (TN) at five different depths with the calibration from the whole depth (0-100 cm) or the shallow layers (0-40 cm) and compare its performance with visible and near-infrared spectroscopy (vis-NIR, 350-2500 nm). A total of 90 soil samples containing 450 subsamples (0-10 cm, 10-20 cm, 20-40 cm, 40-70 cm, and 70-100 cm depths) and their corresponding MIR and vis-NIR spectra were collected from a field of black soil in Northeast China. Multivariate adaptive regression splines (MARS) were used to build prediction models. The results showed that prediction models based on MIR (OM: RMSEp = 1.07-3.82 g/kg, RPD = 1.10-5.80; TN: RMSEp = 0.11-0.15 g/kg, RPD = 1.70-4.39) outperformed those based on vis-NIR (OM: RMSEp = 1.75-8.95 g/kg, RPD = 0.50-3.61; TN: RMSEp = 0.12-0.27 g/kg; RPD = 1.00-3.11) because of the higher number of characteristic bands. Prediction models based on the whole depth calibration (OM: RMSEp = 1.09-2.97 g/kg, RPD = 2.13-5.80; TN: RMSEp = 0.08-0.19 g/kg, RPD = 1.86-4.39) outperformed those based on the shallow layers (OM: RMSEp = 1.07-8.95 g/kg, RPD = 0.50-3.93; TN: RMSEp = 0.11-0.27 g/kg, RPD = 1.00-2.24) because the soil sample data of the whole depth had a larger and more representative sample size and a wider distribution. However, prediction models based on the whole depth calibration might provide lower accuracy in some shallow layers. Accordingly, it is suggested that the methods pertaining to soil property prediction based on the spectral library should be considered in future studies for an optimal approach to predicting soil properties at specific depths. This study verified the superiority of MIR for soil property prediction at specific depths and confirmed the advantage of modeling with the whole depth calibration, pointing out a possible optimal approach and providing a reference for predicting soil properties at specific depths.

Rapid Identification Method for CH4/CO/CH4-CO Gas Mixtures Based on Electronic Nose.

The inherent cross-sensitivity of semiconductor gas sensors makes them extremely challenging to accurately detect mixed gases. In order to solve this problem, this paper designed an electronic nose (E-nose) with seven gas sensors and proposed a rapid method for identifying CH4, CO, and their mixtures. Most reported methods for E-nose were based on analyzing the entire response process and employing complex algorithms, such as neural network, which result in long time-consuming processes for gas detection and identification. To overcome these shortcomings, this paper firstly proposes a way to shorten the gas detection time by analyzing only the start stage of the E-nose response instead of the entire response process. Subsequently, two polynomial fitting methods for extracting gas features are designed according to the characteristics of the E-nose response curves. Finally, in order to shorten the time consumption of calculation and reduce the complexity of the identification model, linear discriminant analysis (LDA) is introduced to reduce the dimensionality of the extracted feature datasets, and an XGBoost-based gas identification model is trained using the LDA optimized feature datasets. The experimental results show that the proposed method can shorten the gas detection time, obtain sufficient gas features, and achieve nearly 100% identification accuracy for CH4, CO, and their mixed gases.

Correlation Between Body Mass Index and Anterolateral Thigh Flap Thickness: A Retrospective Study From a Single Center in China.

Background: During repair of oral and maxillofacial soft tissue defects, organ function is largely related to the amount of thickness of the flap. However, there are few studies on the influencing factors of the thickness of the flap. In this retrospective study, we aim to explore the correlation between body mass index (BMI) and anterolateral thigh (ALT) flap thickness by computed tomography (CT) and ultrasound and provide guidance for evaluating the ALT flap thickness before surgery. Methods: We selected three points A, B, and C on ALT flap and two skilled clinicians measured the thickness of these points. Age and gender as covariates and evaluated by the Chi-square analysis. Inter-group differences between the two BMI groups were examined by the student t test. Intra-group differences within each BMI group were tested by ANOVA. Linear regression analysis was performed to examine the relationship between BMI and ALT flap thickness. Results: One hundred sixty patients measured by CT were included in this study, and the ALT flap thickness measured by CT were 8.96 mm and 11.00 mm (P < 0.0001, t test) at point B in groups with BMI<24.0 and BMI≥24.0, respectively. The thicknesses at points A, B, and C were significantly correlated with the BMI (P < 0.001, correlation analysis, r = 0.462, 0.372, and 0.349 at the points A, B, and C, retrospectively, Pearson test). Conclusion: There was a significant correlation between the ALT flap thickness and BMI. A higher BMI was correlated with a thicker ALT flap.

Combined assay of Circulating Tumor DNA and Protein Biomarkers for early noninvasive detection and prognosis of Non-Small Cell Lung Cancer.

Purpose: Early diagnosis of lung cancer is critical to curtailing cancer-related deaths. We aimed to develop a highly sensitive assay for the analysis of circulating tumor DNA (ctDNA) to detect non-small cell lung cancer (NSCLC) in the early stages. Materials and Methods: We detected EGFR and KRAS mutations in paired plasma and tumor tissue samples from 147 NSCLC patients. Of these, EGFR/KRAS ctDNA mutations and protein biomarkers were comparatively analyzed in 87 individuals. In addition, tissue samples of 20 patients were subjected to repeat multi-gene detection, and pre- and post-operative paired samples of 28 patients were subjected to multi-gene detection. Clinical information was obtained to complement the prognostic value of the combined assay results and post-operative new ctDNA mutation status. Results: EGFR/KRAS mutations were highly consistent in ctDNA and tumor DNA. Combining the detection of EGFR and KRAS mutations in ctDNA with the detection of protein biomarkers increased cancer detection sensitivity to 74.7% (65/87). None of the healthy controls tested positive using the combined assay (100% specificity). Combined assay results independently associated with recurrence-free survival. Post-operative new ctDNA mutation status independently associated with overall survival and recurrence-free survival. Conclusion: The detection of ctDNA may be exploited for early diagnosis of NSCLC, as highlighted by the developed assay. Further, the combined assay results and post-operative new ctDNA mutation status are promising prognostic indicators in NSCLC patients.

Controllable assembly of a novel cationic gemini surfactant containing a naphthalene and amide spacer with ß-cyclodextrin.

A novel cationic gemini surfactant (C12NDDA) with a spacer containing naphthalene and amides was successfully synthesized. The assembly of C12NDDA with ß-cyclodextrin (ß-CD) was investigated using various techniques including transmission electron microscopy, proton nuclear magnetic resonance (1H NMR), and scanning electron microscopy. Tuning the C12NDDA concentration and the C12NDDA/ß-CD molar ratio allowed the production of different assembled aggregate morphologies such as micelles, vesicles, nanowires, nanorods, and hydrogels. Investigation of the inclusion mechanisms of C12NDDA and ß-CD by 1H NMR revealed that hydrophobic interactions, hydrogen bonding, π-π stacking, and electrostatic forces play key roles in the assembly process. The antimicrobial activities of the C12NDDA/xß-CD (x = 0-4) inclusion complexes were tested against Gram-negative bacteria (Escherichia coli and Salmonella) and Gram-positive bacteria (Staphylococcus aureus and Streptococcus), and very low minimum inhibitory concentrations of 0.078-0.31 µg mL-1 were observed. Thus, this newly synthesized gemini surfactant and its inclusion complexes exhibit potential as superior broad-spectrum disinfectants for various biomedical and biotechnological applications.

Size fractions of dust and amount of associated metals on leaf surface and inner wax of 15 plant species at Beijing roadside.

To provide more insight into the removal ability of urban air dust and associated metals by plant leaves, and thus guide urban green planning to improve air quality, 15 plant species leaves collected from Beijing roadside were analyzed for size fractions of leaf surface dust (SD) and inner wax dust (WD). Seven associated metals Cd, Cr, Cu, Fe, Mn, Pb and Zn were also measured. Metal Accumulation Index (MAI) was calculated for different species leaves at various dust sizes and soluble forms, respectively. Cluster analysis was used for the plant species and correlations between dust and metal concentrations and for inter-metal concentrations were calculated for both surface and inner wax dust. Mean leaf total dust TD (SD + WD), SD and WD were measured as 1159, 817 and 342 mg m-2, respectively, with the highest values observed all in Euonymus japonicus. Most species leaves collected larger ratios of SD than WD except Salix babylonica and Robinia pseudoacacia. While SD was presented at all particle size fractions for all plants, nearly all species leaves collected higher proportions of WD >10 µm. Mean metal levels of leaf TD of all species ranged from high to low as Fe > Cr > Zn > Pb > Cu > Mn > Cd, but with different orders for individual species. Metals were observed in all sizes of SD/WD, although the size distributions were various for certain metals. Intercorrelations of metal concentrations in leaf SD/WD were positively significant except Pb, which may have different emission sources. Species Prunus cerasifera f. atropurpurea, Syringa oblata, Malus micromalu, Koelreuteria paniculata and Robinia pseudoacacia may possess better overall metal collection ability due to their relatively higher MAI values, but species Euonymus japonicus, Malus micromalu, Ligustrum x vicaryi and Koelreuteria paniculata were identified as the best choices in removing air dust based on cluster analysis and suggested to be planted at heavy trafficked road site for air quality improvement.

[Effect of diabetic management modes on diabetic nephropathy: a prospective study].

OBJECTIVE: To explore the effect of "diabetes specialists-community general practitioners-community nurse co-management mode" and "diabetes specialist management mode" on diabetic nephropathy (DN) in primary medical institutions. METHODS: Patients with type 2 diabetes admitted to Quanzijie Health Clinic of Jimusar County of Xinjiang Uygur Autonomous Region from October 2017 to March 2018 were enrolled. The Patients were divided into co-management group or specialist management group according to their administrative villages. The treatment plans of the two groups were formulated with reference to the current guidelines. The subjects of the co-management group were jointly managed by a fixed team composed of diabetes specialists from Jimusar Traditional Chinese Medicine Hospital, community general practitioners and community nurses from Quanzijie Health Clinic, and required to attend diabetes education courses every month. The diabetes specialist of Jimusar Traditional Chinese Medicine Hospital was responsible for the formulation and management of the treatment plan of the research object. Follow-up was fulfilled once every 4 weeks for 24 weeks in two groups. Before and after intervention, blood glucose, blood pressure, urinary albumin/creatinine ratio (UACR), estimated glomerular filtration rate (eGFR) as well as the utilization rate of angiotensin converting enzyme inhibitors/angiotensin II receptor blocker (ACEI/ARB) were collected. RESULTS: A total of 115 patients accomplished this study with 54 patients in co-management group and 61 patients in specialist management group. After 24 weeks of intervention, fasting glucose level, postprandial glucose level 2 hours after breakfast, glycosylated hemoglobin (HbA1c), Log UACR in co-management group and specialists management group were significantly decreased compared with baseline [fasting glucose level (mmol/L): 8.06±1.92 vs. 9.16±2.83, 8.21±2.10 vs. 9.06±1.89; postprandial glucose level 2 hours after breakfast (mmol/L): 12.26±3.78 vs. 14.11±5.28, 12.47±3.63 vs. 14.00±3.88; HbA1c: 0.074±0.014 vs. 0.082±0.023, 0.076±0.014 vs. 0.081±0.016; Log UACR (mg/g): 1.63±1.56 vs. 2.25±1.44, 1.84±1.65 vs. 2.43±1.56, all P < 0.05], but there was no statistical significance between the two groups [fasting glucose level (mmol/L): -1.10±0.47 vs. -0.85±0.36, postprandial glucose level 2 hours after breakfast (mmol/L): -1.85±0.88 vs. -1.53±0.68, HbA1c: -0.008±0.004 vs. -0.006±0.003, Log UACR (mg/g): -0.61±0.29 vs. -0.59±0.29, all P < 0.05]. There were no significant changes in blood pressure, serum creatinine and eGFR in the two groups before and after intervention. There were 18 and 24 patients with hypertension in co-management group and specialist management group, respectively. The utilization rates of ACEI/ARB in both groups after intervention were significantly higher than those before intervention [88.9% (16/18) vs. 22.2% (4/18), 95.8% (23/24) vs. 29.2% (7/24), both P < 0.01]. At the end of the study, the utilization rate of ACEI/ARB was similar between the two groups [88.9% (16/18) vs. 95.8% (23/24), P > 0.05]. CONCLUSIONS: Both "diabetes specialists-community general practitioners-community nurse co-management mode" and "diabetes specialist management mode" can effectively decrease glucose levels and UACR levels of patients with type 2 diabetes as well as the standard use of antihypertensive agents, which has positive effects on the prevention and treatment on DN.

miR-17-5p-CXCL14 axis related transcriptome profile and clinical outcome in diffuse gliomas.

GBM tissues are comprised of not only tumor cells but also tumor-associated nontumor cells, such as stromal cells and immune cells, which dilute the purity of glioma cells and function in glioma biology. However, the roles of miRNAs in modulating glioma purity are not clarified. In total, 838 glioma samples with transcriptome data, including 537 RNAseq data from TCGA project and 301 microarray data from Chinese Glioma Genome Atlas (CGGA project), were recruited into our investigation. Tumor purity, molecular subtypes and IDH status were also available. R language was employed as the main tool for statistical analysis and graphical work. Screening miRNA profiling and paired TCGA samples' transcriptome data demonstrates that miR-17-5p expression harbors the most significant positive correlation with glioma purity among all miRNAs. CXCL14 shows robust negative correlation with miR-17-5p expression in TCGA and CGGA dataset. miR-17-5p directly targets CXCL14 and functions as a tumor-suppressor of GBM. CXCL14 showed lower expression in proneural subtype and may contribute as a potential marker for proneural subtype in glioma. Genes markedly correlated with CXCL14 are involved in essential functions associated with anti-tumor immune process. CXCL14 has a strong correlation with immune(T cells, Monocytic lineage and Neutrophils) and Fibroblasts within glioma environment. miR-17-5p and CXCL14 exhibited predictive values for high-grade glioma(HGG) patients: Higher miR-17-5p indicated significantly longer survival while lower CXCL14 indicated longer survival. Our results highlight the importance of the miR-17-5p-CXCL14 axis in regulating key steps of anti-tumor immune process and may serve as potential targets of immune treatments for gliomas.

Different protein-protein interface patterns predicted by different machine learning methods.

Different types of protein-protein interactions make different protein-protein interface patterns. Different machine learning methods are suitable to deal with different types of data. Then, is it the same situation that different interface patterns are preferred for prediction by different machine learning methods? Here, four different machine learning methods were employed to predict protein-protein interface residue pairs on different interface patterns. The performances of the methods for different types of proteins are different, which suggest that different machine learning methods tend to predict different protein-protein interface patterns. We made use of ANOVA and variable selection to prove our result. Our proposed methods taking advantages of different single methods also got a good prediction result compared to single methods. In addition to the prediction of protein-protein interactions, this idea can be extended to other research areas such as protein structure prediction and design.

A chiral BINOL-based Gemini amphiphilic gelator and its specific discrimination of native arginine by gelation in water.

A novel axially chiral cationic Gemini amphiphile gelator (S1) derived from (S)-BINOL has been synthesized and characterized by 1H NMR, 13C NMR, ESI-MS and FT-IR analyses. The critical micelle concentration (CMC) of S1 was determined to be 0.21 mM in water at room temperature. A transparent hydrogel with S1 at 43 mM was obtained at room temperature and characterized using various methods including SEM, CD, fluorescence, 1H NMR, FT-IR, and XRD. The results indicate that the hydrophobic effect of long alkyl chains, π-π stacking of naphthalene rings, and intermolecular hydrogen-bonding of the amide groups of S1 should be responsible for the hydrogel formation. Moreover, an 8.5 mM aqueous solution of S1 could gel by the addition of l-arginine, whereas it failed to gel in the presence of other 15 amino acids, respectively. It is suggested that S1 could discriminate native arginine by hydrogel formation, mainly due to the electrostatic interaction and hydrogen bonding effects between S1 and l-arginine molecules.

Post surgery circulating free tumor DNA is a predictive biomarker for relapse of lung cancer.

Cancer cells release DNA fragments into plasma as circulating free DNA (cfDNA). However, quantitative measurement of tumor-derived DNA in cfDNA remains challenge. The purpose of this study was to quantitatively assess tumor-derived DNA in lung cancer patients. By optimizing competitive allele-specific TaqMan PCR (CAST-PCR), we assessed the copy number of mutated Kirsten rat sarcoma viral oncogene homolog (KRAS) and epidermal growth factor receptor (EGFR) alleles in the pre/post surgery plasma of 168 lung cancer patients. An absolute quantitative PCR method was developed to assess the number of total cfDNA. All mutations detected in tumors were also found in the plasma after surgery. At the time of 30 days after surgery, EGFR mutation of circulating cell-free DNA was detected only in two patients who recurred in 4 months after surgery. Compared to that of normal control at 30 days after surgery, five patients who recurred in 4 months had significantly higher circulating cell-free DNA (P < 0.001), whereas six patients who recurred after 4 months (P = 0.207) and five patients without recurrence (P = 0.901) demonstrated significantly lower circulating cell-free DNA. Our findings suggest that cfDNA analysis in plasma is an alternative and supplement to tissue analysis and hold promise for clinical application. Stratification of patients according to cfDNA levels at 30 days after surgery might be helpful in selecting lung cancer patients for adjuvant therapy after surgery.

Receptor modelling study of polycyclic aromatic hydrocarbons in Jeddah, Saudi Arabia.

Measurements of 14 polycyclic aromatic hydrocarbons (PAH) have been made in Jeddah, Saudi Arabia, with a view to establishing the concentrations in this major city, and quantifying the contributions of major sources. Particulate and vapour forms have been sampled and analysed separately. The concentrations are compared to measurements from other sites in the Middle Eastern region and are towards the lower end of the range, being far lower than concentrations reported from Riyadh (Saudi Arabia), Assiut (Egypt) and Tehran (Iran) but broadly similar to those measured in Damascus (Syria) and higher than those measured in Kuwait. The partitioning between vapour and particle phases is similar to that in data from Egypt and China, but with many compounds showing a higher particle-associated percentage than in Birmingham (UK) possibly reflecting a higher concentration of airborne particulate matter in the former countries. Concentrations in Jeddah were significantly higher at a site close to the oil refinery and a site close to a major ring road than at a suburban site to the north of the city. Application of positive matrix factorisation to the pooled data elicited three factors accounting respectively for 17%, 33% and 50% of the measured sum of PAH and these are interpreted as arising from gasoline vehicles, industrial sources, particularly the oil refinery, and to diesel/fuel oil combustion.

Recombinant Goat VEGF164 Increases Hair Growth by Painting Process on the Skin of Shaved Mouse.

To detect goat vascular endothelial growth factor (VEGF)-mediated regrowth of hair, full-length VEGF164 cDNA was cloned from Inner Mongolia cashmere goat (Capra hircus) into the pET-his prokaryotic expression vector, and the recombinant plasmid was transferred into E. coli BL21 cells. The expression of recombinant 6×his-gVEGF164 protein was induced by 0.5 mM isopropyl thio-ß-D-galactoside at 32°C. Recombinant goat VEGF164 (rgVEGF164) was purified and identi ed by western blot using monoclonal anti-his and anti-VEGF antibodies. The rgVEGF164 was smeared onto the dorsal area of a shaved mouse, and we noted that hair regrowth in this area was faster than in the control group. Thus, rgVEGF164 increases hair growth in mice.

Adjusting for High-dimensional Covariates in Sparse Precision Matrix Estimation by ℓ1-Penalization.

Motivated by the analysis of genetical genomic data, we consider the problem of estimating high-dimensional sparse precision matrix adjusting for possibly a large number of covariates, where the covariates can affect the mean value of the random vector. We develop a two-stage estimation procedure to first identify the relevant covariates that affect the means by a joint ℓ1 penalization. The estimated regression coefficients are then used to estimate the mean values in a multivariate sub-Gaussian model in order to estimate the sparse precision matrix through a ℓ1-penalized log-determinant Bregman divergence. Under the multivariate normal assumption, the precision matrix has the interpretation of a conditional Gaussian graphical model. We show that under some regularity conditions, the estimates of the regression coefficients are consistent in element-wise ℓ∞ norm, Frobenius norm and also spectral norm even when p ≫ n and q ≫ n. We also show that with probability converging to one, the estimate of the precision matrix correctly specifies the zero pattern of the true precision matrix. We illustrate our theoretical results via simulations and demonstrate that the method can lead to improved estimate of the precision matrix. We apply the method to an analysis of a yeast genetical genomic data.

Estimation of the contributions of brake dust, tire wear, and resuspension to nonexhaust traffic particles derived from atmospheric measurements.

Size-fractionated samples of airborne particulate matter have been collected in a number of campaigns at Marylebone Road, London and simultaneously at background sites either in Regents Park or at North Kensington. Analysis of these samples has enabled size distributions of total mass and of a number of elements to be determined, and roadside increments attributable to nonexhaust emissions arising from traffic activity have been calculated. Taking a novel approach, the combined use of size distribution information and tracer elements has allowed the separate estimation of the contributions of brake dust, tire dust, and resuspension to particle mass in the range 0.9-11.5 µm aerodynamic diameter and mean contributions (± s.e.) at the Marylebone Road sampling site are estimated as resuspended dust 38.1 ± 9.7%, brake dust 55.3 ± 7.0%, and tire dust 10.7 ± 2.3%, (accounting for a total of 104.1% of coarse particle mass in the traffic increment above background).

Model Selection and Estimation in the Matrix Normal Graphical Model.

Motivated by analysis of gene expression data measured over different tissues or over time, we consider matrix-valued random variable and matrix-normal distribution, where the precision matrices have a graphical interpretation for genes and tissues, respectively. We present a l(1) penalized likelihood method and an efficient coordinate descent-based computational algorithm for model selection and estimation in such matrix normal graphical models (MNGMs). We provide theoretical results on the asymptotic distributions, the rates of convergence of the estimates and the sparsistency, allowing both the numbers of genes and tissues to diverge as the sample size goes to infinity. Simulation results demonstrate that the MNGMs can lead to better estimate of the precision matrices and better identifications of the graph structures than the standard Gaussian graphical models. We illustrate the methods with an analysis of mouse gene expression data measured over ten different tissues.

A SPARSE CONDITIONAL GAUSSIAN GRAPHICAL MODEL FOR ANALYSIS OF GENETICAL GENOMICS DATA.

Genetical genomics experiments have now been routinely conducted to measure both the genetic markers and gene expression data on the same subjects. The gene expression levels are often treated as quantitative traits and are subject to standard genetic analysis in order to identify the gene expression quantitative loci (eQTL). However, the genetic architecture for many gene expressions may be complex, and poorly estimated genetic architecture may compromise the inferences of the dependency structures of the genes at the transcriptional level. In this paper, we introduce a sparse conditional Gaussian graphical model for studying the conditional independent relationships among a set of gene expressions adjusting for possible genetic effects where the gene expressions are modeled with seemingly unrelated regressions. We present an efficient coordinate descent algorithm to obtain the penalized estimation of both the regression coefficients and sparse concentration matrix. The corresponding graph can be used to determine the conditional independence among a group of genes while adjusting for shared genetic effects. Simulation experiments and asymptotic convergence rates and sparsistency are used to justify our proposed methods. By sparsistency, we mean the property that all parameters that are zero are actually estimated as zero with probability tending to one. We apply our methods to the analysis of a yeast eQTL data set and demonstrate that the conditional Gaussian graphical model leads to more interpretable gene network than standard Gaussian graphical model based on gene expression data alone.

NONPARAMETRIC COVARIANCE MODEL.

There has been considerable attention on estimation of conditional variance function in the literature. We propose here a nonparametric model for conditional covariance matrix. A kernel estimator is developed accordingly, its asymptotic bias and variance are derived, and its asymptotic normality is established. A real data example is used to illustrate the proposed estimation procedure.

Chemical speciation of PM2.5 particles at urban background and rural sites in the UK atmosphere.

Fine particle (PM(2.5)) samples have been collected at two sites in the UK West Midlands, representing urban background (EROS) and rural (CPSS) locations. Chemical quantification has been carried out for major anions, metal species, total OC and EC, and for individual organic marker species including n-alkanes, hopanes, PAHs, organic acids and sterols. Concentration levels of each chemical group are presented and the patterns of individual species used to infer predominant sources of atmospheric fine particles from atmospheric measurements. Temporal and spatial variations are also discussed. The annual mean PM(2.5) concentrations were 11.6 and 10.5 microg m(-3), with large daily variations (2.7-37.1 microg m(-3)) at the two sites. Elevated PM(2.5) was mostly attributed to higher sulfate > OC > nitrate in summer and OC > nitrate > EC in winter. The mean levels of soil/dust related metals, Al > Si > Fe > Ca > Ti, were mostly different from other European sites due to differences in sampling site, local geology or climate, but comparable with earlier UK data. Trace metals, Zn > Cu > Pb > Mn > Ni, mostly showed lower concentrations compared with concentrations measured at the UK national metals network sites, except for Cu and Pb. The concentrations of PAHs were in a similar range to those previously measured within the UK, with an elevation at the urban site and in winter due to increased combustion activities, particularly for the two natural gas-related Oxy-PAHs. n-Alkanes showed a higher contribution in winter (low carbon preference index: 1.5-1.6) from anthropogenic emissions but in summer (high carbon preference index: 2.7-2.9) from biogenic sources, which are appreciable at our rural site (C(max) = 29). Similar levels of hopanes were observed at the urban (0.08-0.24 ng m(-3)) and the rural (0.07-0.23 ng m(-3)) site, with higher winter values at EROS as expected but opposite for CPSS. The concentrations of organic acids ranged from 0.19-10.2 ng m(-3) at EROS and 0.26-6.0 ng m(-3) at CPSS, with mixed patterns of seasonal variation (W/S: 0.54-2.0) for individual aliphatic and aromatic carboxylic acids, except the two resin acids which exhibited much higher levels in winter (W/S: 1.7-3.3) as expected due to higher wood smoke emissions. Cholesterol showed very low annual mean concentrations (0.13-0.16 ng m(-3)) confirming that meat cooking is not an important source at our sites.