Rare earth metals detection and recognition based on laser induced breakdown spectroscopy and machine learning.

The rapid detection and identification of the electronic waste (e-waste) containing rare earth (RE) elements is of great significance for the recycling of RE elements. However, the analysis of these materials is extremely challenging due to extreme similarities in appearance or chemical composition. In this research, a new system based on laser induced breakdown spectroscopy (LIBS) and machine learning algorithms is developed for identifying and classifying e-waste of rare-earth phosphors (REPs). Three different kinds of phosphors are selected and the spectra is monitored using this new developed system. The analysis of phosphor spectra shows that there are Gd, Yd, and Y RE element spectra in the phosphor. The results also verify that LIBS could be used to detect RE elements. An unsupervised learning method, principal component analysis (PCA), is used to distinguish the three phosphors and training data set is stored for further identification. Additionally, a supervised learning method, backpropagation artificial neural network (BP-ANN) algorithm is used to establish a neural network model to identify phosphors. The result show that the final phosphor recognition rate reaches 99.9%. The innovative system based on LIBS and machine learning (ML) has the potential to improve rapid in situ detection of RE elements for the classification of e-waste.

Transcriptional regulation mechanism of wheat varieties with different nitrogen use efficiencies in response to nitrogen deficiency stress.

BACKGROUND: As one of the microelements, nitrogen play essential roles in cereal production. Although the use of chemical fertilizers has significantly improved the yield of wheat, it has also caused increasingly adverse environmental pollution. Revealing the molecular mechanism manipulating wheat nitrogen use efficiency (NUE), and cultivating wheat germplasms with high nitrogen use efficiency has become important goals for wheat researchers. In this study, we investigated the physiological and transcriptional differences of three wheat cultivars with different NUE under low nitrogen stress. RESULTS: The results showed that, under low nitrogen conditions, the activities of nitrogen metabolism-related enzymes (GS, NR, GDH), antioxidant enzymes (SOD, POD, CAT) and soluble protein contents of ZM366 (high NUE cultivar) were higher than those of JD8 (low NUE cultivar). The hybrid cultivar of ZM366 and JD8 showed mid-parent or over-parent heterosis. Transcriptome analysis revealed that 'alanine, aspartate and glutamate metabolism', 'terpenoid backbone biosynthesis' and 'vitamin B6 metabolism' pathways play key roles in nitrogen use efficiency in wheat. The significant enhancement of the 'Calvin cycle' and 'photorespiration' in ZM366 contributed to its higher level of carbon metabolism under low nitrogen stress, which is an important attribute differs from the other two varieties. In addition, the activation of ABA signal transduction and biosynthesis pathways also helps to maintain NUE under low- nitrogen conditions. Moreover, bHLH transcription factors were also found to play a positive role in wheat NUE. CONCLUSIONS: In conclusion, these results enriched our knowledge of the mechanism of wheat NUE, and provided a theoretical basis for improving wheat NUE and breeding new cultivars.

Large area MCP-PMT design with good time performance.

A new large area photomultiplier tube based on the microchannel plates (MCP-PMT) with high collection efficiency (CE) and good time performance is proposed in this paper. A novel focusing system with two cylindrical and a conical barrels is designed to generate the accelerating and focusing electric field. A three-dimensional model is developed by CST Studio Suite to validate its feasibility. Finite Integral Technique and Monte Carlo method are combined to simulate the process. Results predict that CE of the novel MCP-PMT is expected to be 100%. TTS of the photoelectrons from the whole photocathode achieves 1.2 ns. Differ from other large area PMTs, it performs well in the geomagnetic field.

Variational graph auto-encoders for miRNA-disease association prediction.

Cumulative experimental studies have demonstrated the critical roles of microRNAs (miRNAs) in the diverse fundamental and important biological processes, and in the development of numerous complex human diseases. Thus, exploring the relationships between miRNAs and diseases is helpful with understanding the mechanisms, the detection, diagnosis, and treatment of complex diseases. As the identification of miRNA-disease associations via traditional biological experiments is time-consuming and expensive, an effective computational prediction method is appealing. In this study, we present a deep learning framework with variational graph auto-encoder for miRNA-disease association prediction (VGAE-MDA). VGAE-MDA first gets the representations of miRNAs and diseases from the heterogeneous networks constructed by miRNA-miRNA similarity, disease-disease similarity, and known miRNA-disease associations. Then, VGAE-MDA constructs two sub-networks: miRNA-based network and disease-based network. Combining the representations based on the heterogeneous network, two variational graph auto-encoders (VGAE) are deployed for calculating the miRNA-disease association scores from two sub-networks, respectively. Lastly, VGAE-MDA obtains the final predicted association score for a miRNA-disease pair by integrating the scores from these two trained networks. Unlike the previous model, the VGAE-MDA can mitigate the effect of noises from random selection of negative samples. Besides, the use of graph convolutional neural (GCN) network can naturally incorporate the node features from the graph structure while the variational autoencoder (VAE) makes use of latent variables to predict associations from the perspective of data distribution. The experimental results show that VGAE-MDA outperforms the state-of-the-art approaches in miRNA-disease association prediction. Besides, the effectiveness of our model has been further demonstrated by case studies.

Cut-off values of neonatal lysosomal storage disease-related enzymes detected by tandem mass spectrometry.

OBJECTIVE: To establish cut-off values of lysosomal storage disease (LSD)-related enzymes by tandem mass spectrometry. METHODS: A total of 26 689 newborns and 7 clinically confirmed LSD children underwent screening for LSDs (glycogen storage disease typeⅡ, Fabry disease, mucopolysaccharidosis type Ⅰ, Krabbe disease, Niemann-Pick disease A/B and Gaucher disease). The activities of LSD-related enzymes were detected by tandem mass spectrometry. The 20% of the median enzyme activity of each batch of acid ß-glucocerebrosidase, acid sphingomyelinase, ß-galactocerebroside, α- L-iduronidase and acid α-glucosidase, and the 30% of the median enzyme activity of α-galactosidase were taken as cut-off values of corresponding enzymes. The genetic diagnosis was performed in neonates whose enzyme activity was lower than 70% of the cut-off value. RESULTS: The enzyme activities of 7 clinically confirmed cases were all lower than the cut-off values. Among 26 689 newborns, 142 cases (0.53%) were suspected positive for LSDs, including 25 cases of ß-galactocerebroside deficiency, 1 case of α- L-iduronidase deficiency, 19 cases of α-galactosidase deficiency, and 97 cases of acid α-glucosidase deficiency. Eight infants were genetically diagnosed with LSDs, including 3 cases of glycogen storage disease type Ⅱ, 3 cases of Krabbe disease, and 2 cases of Fabry disease, with a positive predictive value of about 5.6%. Cut-off values ​​of the 6 LSD enzyme activities all showed a downward trend from March to August, and an upward trend from September to December. There was a statistically significant difference in LSD enzyme activity among different months ( P<0.05). CONCLUSION: The established cut-off values of LSD-related enzyme activities detected by tandem mass spectrometry can be used for screening LSDs in neonates, and the enzyme activity would be affected by temperature and humidity.

Evaluation of bone metabolism-associated biomarkers in Tibet, China.

AIM: To measure and evaluate the distribution and possible contributing factors of seven bone metabolism-associated biomarkers in Tibet, a plateau province of China. METHODS: A total of 1615 individuals were recruited from Tibet at three different altitudes. The levels and possible contributing factors of serum calcium, serum phosphorus, ALP, 25OHD, PINP, CTX, and PTH were evaluated. RESULTS: In total, 1246 Tibetan adults (males: n = 543) were eventually enrolled in this study. Multiple linear regression recognized age, sex, altitude, and BMI as the major effect factors. The levels of ALP, PINP, and CTX in males continuously decreased with age; however, those in females increased after approximately 39 years of age. Males had higher 25OHD levels (23.9 vs. 15.4 ng/ml) but lower levels of serum phosphorus (1.12 vs. 1.19 mmol/L) and PTH (41.3 vs. 47.4 pg/ml) than females. Before the age of 50, males had higher levels of calcium, ALP, PINP, and CTX than females, and the opposite trend was observed after the age of 50. The highest levels of serum calcium and phosphorus and the lowest levels of PINP and CTX were found in the Shigatse/Lhasa region, suggesting a better bone metabolism status. Compared with reports from plain areas of China, significantly higher levels of PINP (65.3 vs. 49.36 ng/ml) and CTX (0.46 vs. 0.37 ng/ml) were recorded in Tibetan adults. CONCLUSION: A more active bone turnover status was found in Tibetan adults than in individuals from the plain areas of China.

An Angle Recognition Algorithm for Tracking Moving Targets Using WiFi Signals with Adaptive Spatiotemporal Clustering.

An angle estimation algorithm for tracking indoor moving targets with WiFi is proposed. First, phase calibration and static path elimination are proposed and performed on the collected channel state information signals from different antennas. Then, the angle of arrival information is obtained with the joint estimation algorithm of the angle of arrival (AOA) and time of flight (TOF). To deal with the multipath effects, we adopt the DBscan spatiotemporal clustering algorithm with adaptive parameters. In addition, the time-continuous angle of arrival information is obtained by interpolating and supplementing points to extract the dynamic signal paths better. Finally, the least-squares method is used for linear fitting to obtain the final angle information of a moving target. Experiments are conducted with the tracking data set presented with Tsinghua's Widar 2.0. The results show that the average angle estimation error with the proposed algorithm is smaller than Widar2.0. The average angle error is about 7.18° in the classroom environment, 3.62° in the corridor environment, and 12.16° in the office environment; they are smaller than the errors of the existing system.

A People-Counting and Speed-Estimation System Using Wi-Fi Signals.

Counting the number of people and estimating their walking speeds are essential in crowd control and flow. In this work, we propose a system that uses prevalent Wi-Fi signals to identify the number of people entering and leaving a room through a door. It selects the best subcarrier of Wi-Fi signals and applies the Hampel filter to remove outlier information first. Then, it employs a double threshold method to determine the start and end times of entering or leaving. Afterward, it compares the detected signals with the precollected database using the dynamic time-warping algorithm and determines the number of people. It uses a variance threshold method to identify the states of entering or leaving. It also employs a nonlinear fitting approach to calculate the walking speeds. The experiments show that, in a large empty laboratory, the accuracy rates in determining the number of people are 100% for one person, 81% for two persons, and 95% for three persons. In a small office, the accuracy rates for detecting the number of people are 98% for one or two persons, 82% for three persons, 93% for four, and 75% for five persons. For the walking speed estimation, the accuracy rate for a speed error of less than 0.2410 m/s is 75% for a single person.

Ensemble disease gene prediction by clinical sample-based networks.

BACKGROUND: Disease gene prediction is a critical and challenging task. Many computational methods have been developed to predict disease genes, which can reduce the money and time used in the experimental validation. Since proteins (products of genes) usually work together to achieve a specific function, biomolecular networks, such as the protein-protein interaction (PPI) network and gene co-expression networks, are widely used to predict disease genes by analyzing the relationships between known disease genes and other genes in the networks. However, existing methods commonly use a universal static PPI network, which ignore the fact that PPIs are dynamic, and PPIs in various patients should also be different. RESULTS: To address these issues, we develop an ensemble algorithm to predict disease genes from clinical sample-based networks (EdgCSN). The algorithm first constructs single sample-based networks for each case sample of the disease under study. Then, these single sample-based networks are merged to several fused networks based on the clustering results of the samples. After that, logistic models are trained with centrality features extracted from the fused networks, and an ensemble strategy is used to predict the finial probability of each gene being disease-associated. EdgCSN is evaluated on breast cancer (BC), thyroid cancer (TC) and Alzheimer's disease (AD) and obtains AUC values of 0.970, 0.971 and 0.966, respectively, which are much better than the competing algorithms. Subsequent de novo validations also demonstrate the ability of EdgCSN in predicting new disease genes. CONCLUSIONS: In this study, we propose EdgCSN, which is an ensemble learning algorithm for predicting disease genes with models trained by centrality features extracted from clinical sample-based networks. Results of the leave-one-out cross validation show that our EdgCSN performs much better than the competing algorithms in predicting BC-associated, TC-associated and AD-associated genes. de novo validations also show that EdgCSN is valuable for identifying new disease genes.

Enhancing the prediction of disease-gene associations with multimodal deep learning.

MOTIVATION: Computationally predicting disease genes helps scientists optimize the in-depth experimental validation and accelerates the identification of real disease-associated genes. Modern high-throughput technologies have generated a vast amount of omics data, and integrating them is expected to improve the accuracy of computational prediction. As an integrative model, multimodal deep belief net (DBN) can capture cross-modality features from heterogeneous datasets to model a complex system. Studies have shown its power in image classification and tumor subtype prediction. However, multimodal DBN has not been used in predicting disease-gene associations. RESULTS: In this study, we propose a method to predict disease-gene associations by multimodal DBN (dgMDL). Specifically, latent representations of protein-protein interaction networks and gene ontology terms are first learned by two DBNs independently. Then, a joint DBN is used to learn cross-modality representations from the two sub-models by taking the concatenation of their obtained latent representations as the multimodal input. Finally, disease-gene associations are predicted with the learned cross-modality representations. The proposed method is compared with two state-of-the-art algorithms in terms of 5-fold cross-validation on a set of curated disease-gene associations. dgMDL achieves an AUC of 0.969 which is superior to the competing algorithms. Further analysis of the top-10 unknown disease-gene pairs also demonstrates the ability of dgMDL in predicting new disease-gene associations. AVAILABILITY AND IMPLEMENTATION: Prediction results and a reference implementation of dgMDL in Python is available on https://github.com/luoping1004/dgMDL. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Effect of a nanofiltration combined process on the treatment of high-hardness and micropolluted water.

The quality of raw water and the current high level of pollution presents a phenomenon of high hardness and micropollution. An experimental study was conducted of the nanofiltration (NF) pilot-scale process combined with biological contact oxidation precipitation and ultrafiltration (UF) as the pretreatment process to treat this water. The study investigated the removal efficiency and membrane fouling of the NF process under the continuous and stable operating conditions of the combination process and studied the influence of high-hardness water on the membrane pollution of the combination process. The results showed that the combined process had a positive removal effect on conventional pollutants and characteristic pollutants, and the removal rates of conventional pollutants, such as turbidity, UV254 and CODMn, were 95%, 90% and 85%, respectively. The removal efficiency of total hardness, total alkalinity and soluble total solids reached 98%, 86% and 91%, respectively, and that of total desalination was above 95%. The removal rates of fluorescent organic substances, such as tryptophan, tyrosine, soluble microbial products (SMPs), fulvic acid and humus-like substances, as well as the precursors of disinfection byproducts reached over 88% and 50%, respectively. The pollutant removal efficiency of the combined process was mainly concentrated in the NF unit. The pretreatment process had certain removal effects on turbidity and macromolecular organic substances in the raw water, which provided a perfect operating environment for the NF process. Under long-term operation, the main elements of scaling on the surface of the NF membrane included C, O, Na, Mg, Al, Si, S, Cl, Ca, Ti and Fe, which were mainly concentrated at the outlet of the membrane and mainly came from monomers or compounds composed of inorganic salts in the raw water and some organic compounds. High-hardness water accelerated the change in membrane process parameters, and the surface of the membrane had abundant inorganic scaling. The inorganic scale on the surface of the NF membrane increased noticeably when filtering water with high hardness. Regular cleaning of the UF and NF membranes could effectively restore the parameters of the process and prolong the service life of the membrane process.

O-GlcNAc Transferase Regulates Angiogenesis in Idiopathic Pulmonary Arterial Hypertension.

Idiopathic pulmonary arterial hypertension (IPAH) is considered a vasculopathy characterized by elevated pulmonary vascular resistance due to vasoconstriction and/or lung remodeling such as plexiform lesions, the hallmark of the PAH, as well as cell proliferation and vascular and angiogenic dysfunction. The serine/threonine hydroxyl-linked N-Acetylglucosamine (O-GlcNAc) transferase (OGT) has been shown to drive pulmonary arterial smooth muscle cell (PASMC) proliferation in IPAH. OGT is a cellular nutrient sensor that is essential in maintaining proper cell function through the regulation of cell signaling, proliferation, and metabolism. The aim of this study was to determine the role of OGT and O-GlcNAc in vascular and angiogenic dysfunction in IPAH. Primary isolated human control and IPAH patient PASMCs and pulmonary arterial endothelial cells (PAECs) were grown in the presence or absence of OGT inhibitors and subjected to biochemical assessments in monolayer cultures and tube formation assays, in vitro vascular sprouting 3D spheroid co-culture models, and de novo vascularization models in NODSCID mice. We showed that knockdown of OGT resulted in reduced vascular endothelial growth factor (VEGF) expression in IPAH primary isolated vascular cells. In addition, specificity protein 1 (SP1), a known stimulator of VEGF expression, was shown to have higher O-GlcNAc levels in IPAH compared to control at physiological (5 mM) and high (25 mM) glucose concentrations, and knockdown resulted in decreased VEGF protein levels. Furthermore, human IPAH PAECs demonstrated a significantly higher degree of capillary tube-like structures and increased length compared to control PAECs. Addition of an OGT inhibitor, OSMI-1, significantly reduced the number of tube-like structures and tube length similar to control levels. Assessment of vascular sprouting from an in vitro 3D spheroid co-culture model using IPAH and control PAEC/PASMCs and an in vivo vascularization model using control and PAEC-embedded collagen implants demonstrated higher vascularization in IPAH compared to control. Blocking OGT activity in these experiments, however, altered the vascular sprouting and de novo vascularization in IPAH similar to control levels when compared to controls. Our findings in this report are the first to describe a role for the OGT/O-GlcNAc axis in modulating VEGF expression and vascularization in IPAH. These findings provide greater insight into the potential role that altered glucose uptake and metabolism may have on the angiogenic process and the development of plexiform lesions. Therefore, we believe that the OGT/O-GlcNAc axis may be a potential therapeutic target for treating the angiogenic dysregulation that is present in IPAH.

An ultrasensitive and selective electrochemical sensor for determination of estrone 3-sulfate sodium salt based on molecularly imprinted polymer modified carbon paste electrode.

A highly sensitive and selective electrochemical sensor based on carbon paste electrode (CPE) modified with molecularly imprinted polymers (MIPs) has been developed for the determination of estrone 3-sulfate sodium salt (ESS). MIPs were prepared in polar medium via bulk polymerization and characterized by scanning electron microscopy and infrared spectroscopy. Cyclic voltammetry was performed to the study preparation process and binding behavior of the MIP-modified CPE (MIP/CPE) toward ESS. The conditions for preparing MIPs and MIP/CPE as well as ESS detection were optimized. Under the optimal experimental conditions, the detection linear range for ESS is 4 × 10-12 to 6 × 10-9 M with a limit of detection of 1.18 × 10-12 M (S/N = 3). In addition, the sensor exhibits high binding affinity toward ESS over its structural analogues with excellent repeatability and stability. The fabricated MIP/CPE was then successfully employed to detect ESS in pregnant mare urine (PMU) without any pretreatment, and the average recoveries were from 99.6 to 104.9% with relative standard deviation less than 3.0%. High-performance liquid chromatography was adopted as a reference to validate the established approach in detecting ESS and their results showed good agreement. The as-prepared sensor has high potential to be a decent tool for on-site determination of ESS in PMU in a fast and convenient manner. Graphical Abstract ᅟ.

Bone Morphogenic Protein Type 2 Receptor Mutation-Independent Mechanisms of Disrupted Bone Morphogenetic Protein Signaling in Idiopathic Pulmonary Arterial Hypertension.

Altered bone morphogenic protein (BMP) signaling, independent of BMPR2 mutations, can result in idiopathic pulmonary arterial hypertension (IPAH). Glucose dysregulation can regulate multiple processes in IPAH. However, the role of glucose in BMP antagonist expression in IPAH has not been characterized. We hypothesized that glucose uptake regulates BMP signaling through stimulation of BMP antagonist expression in IPAH. Using human plasma, lung tissue, and primary pulmonary arterial smooth muscle cells (PASMCs), we examined the protein expression of BMP2, BMP-regulated Smads, and Smurf-1 in patients with IPAH and control subjects. Gremlin-1 levels were elevated in patients with IPAH compared with control subjects, whereas expression of BMP2 was not different. We demonstrate increased Smad polyubiquitination in IPAH lung tissue and PASMCs that was further enhanced with proteasomal inhibition. Examination of the Smad ubiquitin-ligase, Smurf-1, showed increased protein expression in IPAH lung tissue and localization in the smooth muscle of the pulmonary artery. Glucose dose dependently increased Smurf-1 protein expression in control PASMCs, whereas Smurf-1 in IPAH PASMCs was increased and sustained. Conversely, phospho-Smad1/5/8 levels were reduced in IPAH compared with control PASMCs at physiological glucose concentrations. Interestingly, high glucose concentrations decreased phosphorylation of Smad1/5/8 in control PASMCs. Blocking glucose uptake had opposing effects in IPAH PASMCs, and inhibition of Smurf-1 activity resulted in partial rescue of Smad1/5/8 activation and cell migration rates. Collectively, these data suggest that BMP signaling can be regulated through BMPR2 mutation-independent mechanisms. Gremlin-1 (synonym: induced-in-high-glucose-2 protein) and Smurf-1 may function to inhibit BMP signaling as a consequence of the glucose dysregulation described in IPAH.

O-linked ß-N-acetylglucosamine transferase directs cell proliferation in idiopathic pulmonary arterial hypertension.

BACKGROUND: Idiopathic pulmonary arterial hypertension (IPAH) is a cardiopulmonary disease characterized by cellular proliferation and vascular remodeling. A more recently recognized characteristic of the disease is the dysregulation of glucose metabolism. The primary link between altered glucose metabolism and cell proliferation in IPAH has not been elucidated. We aimed to determine the relationship between glucose metabolism and smooth muscle cell proliferation in IPAH. METHODS AND RESULTS: Human IPAH and control patient lung tissues and pulmonary artery smooth muscle cells (PASMCs) were used to analyze a specific pathway of glucose metabolism, the hexosamine biosynthetic pathway. We measured the levels of O-linked ß-N-acetylglucosamine modification, O-linked ß-N-acetylglucosamine transferase (OGT), and O-linked ß-N-acetylglucosamine hydrolase in control and IPAH cells and tissues. Our data suggest that the activation of the hexosamine biosynthetic pathway directly increased OGT levels and activity, triggering changes in glycosylation and PASMC proliferation. Partial knockdown of OGT in IPAH PASMCs resulted in reduced global O-linked ß-N-acetylglucosamine modification levels and abrogated PASMC proliferation. The increased proliferation observed in IPAH PASMCs was directly impacted by proteolytic activation of the cell cycle regulator, host cell factor-1. CONCLUSIONS: Our data demonstrate that hexosamine biosynthetic pathway flux is increased in IPAH and drives OGT-facilitated PASMC proliferation through specific proteolysis and direct activation of host cell factor-1. These findings establish a novel regulatory role for OGT in IPAH, shed a new light on our understanding of the disease pathobiology, and provide opportunities to design novel therapeutic strategies for IPAH.

[Chemical constituents of ethyl acetate fraction from seed melon (Citrulluslanatus ssp. vulgaris var. megalaspermus)].

In this paper, the chemical composition of ethyl acetate parts of seed melon were studied by using ethanol re-flux method, extraction method, and isolated by column chromatography oversilica gel and Sephadex LH-20 and HPLC. The structures of the separated compounds were identified by physical-chemical methods and spectral data such as MS, ¹H-NMR, ¹³C-NMR, etc. 12 compounds were got from the plant including one new compound, 4-hydroxymet-hyl-2-methoxyphenyl 1-O-ß-D-[6'-O-(4″-hydroxybenzoyl)-glucopyranoside] (1) and 11 known compounds, uracil (2), thymine (3), 2'-deoxyuridine (4), 7,8-dimethylalloxazine (5), indole-3-carboxylic acid (6), ß-adenosine (7), 4-hydroxybenzoic acid (8), p-coumaric acid (9), cucumegastigmanesⅠ (10), 3'-methoxyl-quercetin-7-O-ß-D-glucopyranoside (11) and 3,3'-dimethyloxy-4,4'-dihydroxy-9,9'-monoepoxy lignan (12).

Modification of hyaluronan by heavy chains of inter-α-inhibitor in idiopathic pulmonary arterial hypertension.

We previously reported an altered hyaluronan (HA) metabolism in idiopathic pulmonary arterial hypertension (IPAH) lung tissue and cultured smooth muscle cells. Hyaluronan was present in the smooth muscle cell layer surrounding the pulmonary vasculature and in plexigenic lesions. Additionally, cultured pulmonary artery smooth muscle cells produced spontaneous HA "cable" structures, without additional stimuli, that were leukocyte-adhesive. We now present evidence that the HA that accumulates in IPAH plexigenic lesions is a pathological form of HA in which heavy chains (HCs) from the serum-derived proteoglycan inter-α-inhibitor are covalently attached to the HA backbone to form a pathological HC-HA complex. CD45-positive leukocytes were identified within these HC-HA matrices. Elevated mRNA levels of the enzyme that transfers HCs to HA, known as tumor necrosis factor-stimulated gene 6, were detected in IPAH lung tissue.

Nonlinear-model-based analysis methods for time-course gene expression data.

Microarray technology has produced a huge body of time-course gene expression data and will continue to produce more. Such gene expression data has been proved useful in genomic disease diagnosis and drug design. The challenge is how to uncover useful information from such data by proper analysis methods such as significance analysis and clustering analysis. Many statistic-based significance analysis methods and distance/correlation-based clustering analysis methods have been applied to time-course expression data. However, these techniques are unable to account for the dynamics of such data. It is the dynamics that characterizes such data and that should be considered in analysis of such data. In this paper, we employ a nonlinear model to analyse time-course gene expression data. We firstly develop an efficient method for estimating the parameters in the nonlinear model. Then we utilize this model to perform the significance analysis of individually differentially expressed genes and clustering analysis of a set of gene expression profiles. The verification with two synthetic datasets shows that our developed significance analysis method and cluster analysis method outperform some existing methods. The application to one real-life biological dataset illustrates that the analysis results of our developed methods are in agreement with the existing results.

Typical emerging contaminants in sewage treatment plant effluent, and related watersheds in the Pearl River Basin: Ecological risks and source identification.

Emerging contaminants pose a potential risk to aquatic ecosystems in the Pearl River Basin, China, owing to the high population density and active industry. This study investigated samples from eight sewage treatment plants, and five surface water bodies of related watersheds. To screen the risk of emerging contaminants (ECs), and clarify their sources, this study calculated the risk quotient of detected chemical and performed source identification/apportionment using the positive matrix factorization method. In total, 149 organic pollutants were identified. Pharmaceuticals showed significant concentrations in sewage treatment plant samples (120.87 ng/L), compared with surface water samples (1.13 ng/L). The ecological risk assessment identified three chemicals with a heightened risk to aquatic organisms: fipronil sulfide, caffeine, and roxithromycin. Four principal sources of contaminants were identified: pharmaceutical wastewater, domestic sewage, medical effluent, and agricultural runoff. Pharmaceutical wastewater was the primary contributor (60.4 %), to the cumulative EC concentration and to ECs in sewage treatment plant effluent. Agricultural drainage was the main source of ECs in surface water. This study provides a strategy to obtain comprehensive information on the aquatic risks and potential sources of EC species in areas affected by artificial activities, which is of substantial importance to pollutant management and control.