Longitudinal patterns in carbon and nitrogen fluxes and stream metabolism along an urban watershed continuum

An urban watershed continuum framework hypothesizes that there are coupled changes in (1) carbon and nitrogen cycling, (2) groundwater-surface water interactions, and (3) ecosystem metabolism along broader hydrologic flowpaths. It expands our understanding of urban streams beyond a reach scale. We evaluated this framework by analyzing longitudinal patterns in: C and N concentrations and mass balances, groundwater-surface interactions, and stream metabolism and carbon quality from headwaters to larger order streams. 52 monitoring sites were sampled seasonally and monthly along the Gwynns Falls watershed, which drains 170 km² of the Baltimore Long-Term Ecological Research site. Regarding our first hypothesis of coupled C and N cycles, there were significant inverse linear relationships between nitrate and dissolved organic carbon (DOC) and nitrogen longitudinally (P < 0.05). Regarding our second hypothesis of coupled groundwater-surface water interactions, groundwater seepage and leaky piped infrastructure contributed significant inputs of water and N to stream reaches based on mass balance and chloride/fluoride tracer data. Regarding our third hypothesis of coupled ecosystem metabolism and carbon quality, stream metabolism increased downstream and showed potential to enhance DOC lability (e.g., ~4 times higher mean monthly primary production in urban streams than forest streams). DOC lability also increased with distance downstream and watershed urbanization based on protein and humic-like fractions, with major implications for ecosystem metabolism, biological oxygen demand, and CO₂ production and alkalinity. Overall, our results showed significant in-stream retention and release (0–100 %) of watershed C and N loads over the scale of kilometers, seldom considered when evaluating monitoring, management, and restoration effectiveness. Given dynamic transport and retention across evolving spatial scales, there is a strong need to longitudinally and synoptically expand studies of hydrologic and biogeochemical processes beyond a stream reach scale along the urban watershed continuum.     

Long-term changes in nutrient concentrations of the Changjiang River and principal tributaries

We present long-term nutrient data on the Changjiang River (Yangtze River) at six hydrological stations and eight principal tributaries during the period 1958–1985. Three patterns of temporal changes were observed in nitrate and nitrite : minimal variations in the upper catchment area, rapid increases in the middle watershed towards the end of the 1970s, and a gradual increase in the lower drainage basin. Prior to the 1970s, the level of throughout the Changjiang River system remained fairly constant. In the 1980s, however, this changed, with the lowest values in the upper Changjiang changing rapidly to the highest in the middle reaches and then declining slowly but steadily in the lower courses. Compared to and ammonium and soluble reactive phosphorus (SRP) showed smaller increases or no long-term variations, while dissolved silica (DSi) concentration generally decreased at most stations. These three patterns of and changes in the Changjiang River system were reflective of the difference in chemical fertilizer use and landscape features (e.g., slope, soil type and water body area) of the drainage basins of the primary tributaries. The decreases in DSi were most likely attributed to a reduction in suspended sediment loading due to dam constructions and increasing diatom consumption. The increase in and with a reduction in DSi concentrations in the Changjiang River could have significant effects on the stoichiometric balance of nutrients delivered to the East China Sea and the ecosystem in this dynamic region.     

Potential effects of leaf litter on water quality in urban watersheds

Leaf litter plays a critical role in regulating ecological functions in headwater forest streams, whereas the effects of leaves on water quality in urbanized streams are not fully understood. This study examined the potential importance of leaf litter for the release and transformations of organic carbon and nutrients in urban streams, and compared the effects with other types of natural organic substrates (periphyton and stream sediment). Nutrients and organic carbon were leached from senescent leaves of 6 tree species in the laboratory with deionized water, and maximal releases, leaching rate constants, composition and bioavailability of the leached dissolved organic carbon (DOC) were determined. Stream substrates (leaf debris, rocks with periphyton, and sediment) were seasonally collected from urban and forest reference streams of the NSF Baltimore Long-term Ecological Research Site and incubated with overlying stream water to estimate areal fluxes of DOC and nitrogen. Leaf litter leaching showed large ranges in maximal releases of DOC (7.0–131 mg g^?1), dissolved organic nitrogen (DON; 0.07–1.39 mg g^?1) and total dissolved phosphorus (TDP; 0.14–0.70 mg g^?1) among tree species. DOC leaching rate constants, carbon to nitrogen ratios, and DOC bioavailability were all correlated with organic matter quality indicated by fluorescence spectroscopy. Results from substrate incubation experiments showed far higher DOC and DON release and nitrate retention with leaf debris than with sediment, or rocks with periphyton. DOC release from leaf debris was positively correlated with stream nitrate retention at residential and urban sites, with the highest values observed during the fall and lowest during the summer. This study suggests the potential importance of leaf litter quantity and quality on fostering DOC and nutrient release and transformations in urban streams. It also suggests that species-specific impacts of leaves should be considered in riparian buffer and stream restoration strategies.     

A machine learning approach for the identification of protein secondary structure elements from electron cryo-microscopy density maps

The accuracy of the secondary structure element (SSE) identification from volumetric protein density maps is critical for de-novo backbone structure derivation in electron cryo-microscopy (cryoEM). It is still challenging to detect the SSE automatically and accurately from the density maps at medium resolutions (～5-10 ?). We present a machine learning approach, SSELearner, to automatically identify helices and β-sheets by using the knowledge from existing volumetric maps in the Electron Microscopy Data Bank. We tested our approach using 10 simulated density maps. The averaged specificity and sensitivity for the helix detection are 94.9% and 95.8%, respectively, and those for the β-sheet detection are 86.7% and 96.4%, respectively. We have developed a secondary structure annotator, SSID, to predict the helices and β-strands from the backbone Cα trace. With the help of SSID, we tested our SSELearner using 13 experimentally derived cryo-EM density maps. The machine learning approach shows the specificity and sensitivity of 91.8% and 74.5%, respectively, for the helix detection and 85.2% and 86.5% respectively for the β-sheet detection in cryoEM maps of Electron Microscopy Data Bank. The reduced detection accuracy reveals the challenges in SSE detection when the cryoEM maps are used instead of the simulated maps. Our results suggest that it is effective to use one cryoEM map for learning to detect the SSE in another cryoEM map of similar quality.     

A higher correlation of HCV core antigen with CD4+ T cell counts compared with HCV RNA in HCV/HIV-1 coinfected patients

Development of HCV infection is typically followed by chronic hepatitis C (CHC) in most patients, while spontaneous HCV viral clearance (SVC) occurs in only a minority of subjects. Compared with the widespread application of HCV RNA testing by quantitative RT-PCR technique, HCV core antigen detection may be an alternative indicator in the diagnosis of hepatitis C virus infections and in monitoring the status of infectious individuals. However, the correlation and differences between these two indicators in HCV infection need more investigation, especially in patients coinfected by HIV-1. In this study, a total of 354 anti-HCV and/or anti-HIV serum positive residents from a village of central China were enrolled. Besides HCV-related hepatopathic variables including clinical status, ALT, AST, anti-HCV Abs, as well as the altered CD4+/CD8+ T cell counts, HCV core antigen and HCV viral load were also measured. The concentration of serum HCV core antigen was highly correlated with level of HCV RNA in CHC patients with or without HIV-1 coinfection. Of note, HCV core antigen concentration was negatively correlated with CD4+ T cell count, while no correlation was found between HCV RNA level and CD4+ T cell count. Our findings suggested that quantitative detection of plasma HCV core antigen may be an alternative indicator of HCV RNA qPCR assay when evaluating the association between HCV replication and host immune status in HCV/HIV-1 coinfected patients.     

A bag-of-words approach for Drosophila gene expression pattern annotation

Background  

Drosophila gene expression pattern images document the spatiotemporal dynamics of gene expression during embryogenesis. A comparative analysis of these images could provide a fundamentally important way for studying the regulatory networks governing development. To facilitate pattern comparison and searching, groups of images in the Berkeley Drosophila Genome Project (BDGP) high-throughput study were annotated with a variable number of anatomical terms manually using a controlled vocabulary. Considering that the number of available images is rapidly increasing, it is imperative to design computational methods to automate this task.     

Automated annotation of Drosophila gene expression patterns using a controlled vocabulary

MOTIVATION: Regulation of gene expression in space and time directs its localization to a specific subset of cells during development. Systematic determination of the spatiotemporal dynamics of gene expression plays an important role in understanding the regulatory networks driving development. An atlas for the gene expression patterns of fruit fly Drosophila melanogaster has been created by whole-mount in situ hybridization, and it documents the dynamic changes of gene expression pattern during Drosophila embryogenesis. The spatial and temporal patterns of gene expression are integrated by anatomical terms from a controlled vocabulary linking together intermediate tissues developed from one another. Currently, the terms are assigned to patterns manually. However, the number of patterns generated by high-throughput in situ hybridization is rapidly increasing. It is, therefore, tempting to approach this problem by employing computational methods. RESULTS: In this article, we present a novel computational framework for annotating gene expression patterns using a controlled vocabulary. In the currently available high-throughput data, annotation terms are assigned to groups of patterns rather than to individual images. We propose to extract invariant features from images, and construct pyramid match kernels to measure the similarity between sets of patterns. To exploit the complementary information conveyed by different features and incorporate the correlation among patterns sharing common structures, we propose efficient convex formulations to integrate the kernels derived from various features. The proposed framework is evaluated by comparing its annotation with that of human curators, and promising performance in terms of F1 score has been reported.