Diversity of fungal endophytes isolated from the invasive plant Solanum rostratum

A culture-dependent method was used to isolate fungal endophytes from the leaves, stems, and roots of the invasive plant Solanum rostratum Dunal. growing in Xinjiang Province, China. All isolates were identified according to ITS (internal transcribed spacer) region of ribosomal DNA sequences and analyzed by Nucleotide BLAST according to NCBI GenBank and Mycobank database. Altogether 176 endophytic fungal isolates corresponding to 44 OTUs were identified, which were classified into 12 genera, with Penicillium (59.66%) and Aspergillus (23.29%) being the highly dominant genera. Ten endophytic isolates (OTU1, OTU15, OTU16, OTU21, OTU23, OTU25, OTU26, OTU30, OTU37 and OTU44) were identified as potential new species

Identification of pathogenic fungi causing leaf spot of Urtica cannabina and Malus sieversii in the wild fruit forest of Tianshan Mountain, Xinjiang, China

Degradation of the wild apple trees in the wild fruit forest of Tianshan mountain of Xinjiang Province, China, has attracted great attention in recent years, and pathogens are believed to be an important responsible factor. We observed that Malus sieversii and its understory plant, Urtica cannabina, exhibited similar symptoms of leaf spot disease, and we suspect that they are caused by the same pathogens. DNA sequencing using ITS1 and ITS4 primers was applied to identify the pathogenic fungi from diseased leaves of U. cannabina and M. sieversii, which led to the identification of Alternaria sp. and Fusarium sp. as active pathogens causing same symptoms on leaves of both species. Our results implied that these two plants shared the same pathogenic fungi that cause leaf spot disease, and infection of the understory species U. cannabina might provide a reservoir of the pathogens which can attack M. sieversii and contribute at least in part, to the degradation of M. sieversii

Statistical Prediction of the South China Sea Surface Height Anomaly

Based on the simple ocean data assimilation (SODA) data, this study analyzes and forecasts the monthly sea surface height anomaly (SSHA) averaged over South China Sea (SCS). The approach to perform the analysis is a time series decomposition method, which decomposes monthly SSHAs in SCS to the following three parts: interannual, seasonal, and residual terms. Analysis results demonstrate that the SODA SSHA time series are significantly correlated to the AVISO SSHA time series in SCS. To investigate the predictability of SCS SSHA, an exponential smoothing approach and an autoregressive integrated moving average approach are first used to fit the interannual and residual terms of SCS SSHA while keeping the seasonal part invariant. Then, an array of forecast experiments with the start time spanning from June 1977 to June 2007 is performed based on the prediction model which integrates the above two models and the time-independent seasonal term. Results indicate that the valid forecast time of SCS SSHA of the statistical model is about 7 months, and the predictability of SCS SSHA in Spring and Autumn is stronger than that in Summer and Winter. In addition, the prediction skill of SCS SSHA has remarkable decadal variability, with better phase forecast in 1997-2007

Evaluation of the Impact of Argo Data on Ocean Reanalysis in the Pacific Region

Observing System Simulation Experiments (OSSEs) have been conducted to evaluate the effect of Argo data assimilation on ocean reanalysis in the Pacific region. The “truth” is obtained from a 5-year model integration from 2003 to 2007 based on the MIT general circulation model with the truly varying atmospheric forcing. The “observations” are the projections of the truth onto the observational network including ocean station data, CTD, and various BTs and Argo, by adding white noise to simulate observational errors. The data assimilation method employed is a sequential three-dimensional variational (3D-Var) scheme within a multigrid framework. Results show the interannual variability of temperature, salinity, and current fields can be reconstructed fairly well. The spread of temperature anomalies in the tropical Pacific region is also able to be reflected accurately when Argo data is assimilated, which may provide a reliable initial field for the forecast of temperature and currents for the subsurface in the tropical Pacific region. The adjustment of salinity by using T-S relationship is vital in the tropical Pacific region. However, the adjustment of salinity is almost meaningless in the northwest Pacific if Argo data is included during the reanalysis

Neutrophil Extracellular Traps Promote Inflammatory Responses in Psoriasis via Activating Epidermal TLR4/IL-36R Crosstalk

Epidermal infiltration of neutrophils is a hallmark of psoriasis, where their activation leads to release of neutrophil extracellular traps (NETs). The contribution of NETs to psoriasis pathogenesis has been unclear, but here we demonstrate that NETs drive inflammatory responses in skin through activation of epidermal TLR4/IL-36R crosstalk. This activation is dependent upon NETs formation and integrity, as targeting NETs with DNase I or CI-amidine in vivo improves disease in the imiquimod (IMQ)-induced psoriasis-like mouse model, decreasing IL-17A, lipocalin2 (LCN2), and IL-36G expression. Proinflammatory activity of NETs, and LCN2 induction, is dependent upon activation of TLR4/IL-36R crosstalk and MyD88/nuclear factor-kappa B (NF-κB) down-stream signaling, but independent of TLR7 or TLR9. Notably, both TLR4 inhibition and LCN2 neutralization alleviate psoriasis-like inflammation and NETs formation in both the IMQ model and K14-VEGF transgenic mice. In summary, these results outline the mechanisms for the proinflammatory activity of NETs in skin and identify NETs/TLR4 as novel therapeutic targets in psoriasis

Chemical Composition, Phytotoxic, Antimicrobial and Insecticidal Activity of the Essential Oils of Dracocephalum integrifolium

The present investigation studied the chemical composition of the essential oils extracted from Dracocephalum integrifolium Bunge growing in three different localities in northwest China and evaluated the phytotoxic, antimicrobial and insecticidal activities of the essential oils as well as their major constituents, i.e., sabinene and eucalyptol. GC/MS analysis revealed the presence of 21–24 compounds in the essential oils, representing 94.17–97.71% of the entire oils. Monoterpenes were the most abundant substances, accounting for 85.30–93.61% of the oils; among them, sabinene (7.35–14.0%) and eucalyptol (53.56–76.11%) were dominant in all three oils, which occupied 67.56–83.46% of the total oils. In general, phytotoxic bioassays indicated that the IC50 values of the oils and their major constituents were below 2 μL/mL (1.739–1.886 mg/mL) against Amaranthus retroflexus and Poa annua. Disc diffusion method demonstrated that the oils and their major constituents possessed antimicrobial activity against Bacillus subtilis, Pseudomonas aeruginosa, Escherichia coli, Saccharomyces cerevisiae, and Candida albicans, with MIC values ranging from 5–40 μL/mL (4.347–37.712 mg/mL). The oils, sabinene and eucalyptol also exhibited significant pesticidal activity, with the mortality rates of Aphis pomi reaching 100% after exposing to 10 μL oil/petri dish (8.694–9.428 mg/petri dish) for 24 h. To the best of our knowledge, this is the first report on the chemical composition, phytotoxic, antimicrobial and insecticidal activity of the essential oils extracted from D. integrifolium; it is noteworthy to mention that this is also the first report on the phytotoxicity of one of the major constituents, sabinene. Our results imply that D. integrifolium oils and sabinene have the potential value of being further exploited as natural pesticides

Impact of Argo Observation on the Regional Ocean Reanalysis of China Coastal Waters and Adjacent Seas: A Twin-Experiment Study

A regional ocean reanalysis system of China coastal waters and adjacent seas, called CORA (China ocean reanalysis), has been recently developed at the National Marine Data and Information Service (NMDIS). In this study, based on CORA, the impact of Argo profiles on the regional reanalysis is evaluated using a twin-experiment approach. It is found that, by assimilating Argo observations, the reanalysis quality is much improved: the root mean square (RMS) error of temperature and salinity can be further reduced by about 10% and the RMS error of current can be further reduced by 18%, compared to the case only assimilating conventional in situ temperature and salinity observations. Consistent with the unique feature of Argo observations, the temperature is improved in all levels and the largest improvement of salinity happens in the deep ocean. Argo profile data have a significant impact on the regional ocean reanalysis through improvements of both hydrographic and dynamic fields

Statistical Prediction of the South China Sea Surface Height Anomaly

Based on the simple ocean data assimilation (SODA) data, this study analyzes and forecasts the monthly sea surface height anomaly (SSHA) averaged over South China Sea (SCS). The approach to perform the analysis is a time series decomposition method, which decomposes monthly SSHAs in SCS to the following three parts: interannual, seasonal, and residual terms. Analysis results demonstrate that the SODA SSHA time series are significantly correlated to the AVISO SSHA time series in SCS. To investigate the predictability of SCS SSHA, an exponential smoothing approach and an autoregressive integrated moving average approach are first used to fit the interannual and residual terms of SCS SSHA while keeping the seasonal part invariant. Then, an array of forecast experiments with the start time spanning from June 1977 to June 2007 is performed based on the prediction model which integrates the above two models and the time-independent seasonal term. Results indicate that the valid forecast time of SCS SSHA of the statistical model is about 7 months, and the predictability of SCS SSHA in Spring and Autumn is stronger than that in Summer and Winter. In addition, the prediction skill of SCS SSHA has remarkable decadal variability, with better phase forecast in 1997–2007