Atypical seasonal variability of the Kuroshio Current affected by intraseasonal signals at its origin based on direct mooring observations.

The spatial distribution and temporal variability of the Kuroshio Current (KC) was investigated with three moorings deployed at 122.7° E, 123° E, and 123.3° E along 18° N from January 2018 to the spring of 2020. It is shown that the core of the KC is located to the west of 122.7° E along 18° N. With the increase in longitude, the KC extended its vertical scale and attenuated its intensity gradually. The satellite data indicated that the KC was strongest in winter and spring, while it was weakest in autumn along 18° N. However, the seasonal cycle of the KC from mooring observations was atypical compared with that from the satellite data. The seasonal variation of the KC was not obvious in 2018, and a summer peak of KC occurred in 2019. The atypical seasonal variability of the KC was attributed to the strong intraseasonal signals generated by eddy activity. Eddies propagated from east and were enhanced to the west of 140° E, leading to the westward intensified intraseasonal signals. In addition, the intraseasonal signals varied interannually, that is why the variation of the KC in 2018 was quite different with that in 2019.

Prediction of occult tumor progression via platelet RNAs in a mouse melanoma model: a potential new platform for early detection of cancer.

BACKGROUND: Cancer screening provides the opportunity to detect cancer early, ideally before symptom onset and metastasis, and offers an increased opportunity for a better prognosis. The ideal biomarkers for cancer screening should discriminate individuals who have not developed invasive cancer yet but are destined to do so from healthy subjects. However, most cancers lack effective screening recommendations. Therefore, further studies on novel screening strategies are urgently required. METHODS: We used a simple suboptimal inoculation melanoma mouse model to obtain 'pre-diagnostic samples' of mice with macroscopic melanomas. High-throughput sequencing and bioinformatic analysis were employed to identify differentially expressed RNAs in platelet signatures of mice injected with a suboptimal number of melanoma cells (eDEGs) compared with mice with macroscopic melanomas and negative controls. Moreover, 36 genes selected from the eDEGs via bioinformatics analysis were verified in a mouse validation cohort via quantitative real-time PCR. LASSO regression was utilized to generate the prediction models with gene expression signatures as the best predictors for occult tumor progression in mice. RESULTS: These RNAs identified from eDEGs of mice injected with a suboptimal number of cancer cells were strongly enriched in pathways related to immune response and regulation. The prediction models generated by 36 gene qPCR verification data showed great diagnostic efficacy and predictive value in our murine validation cohort, and could discriminate mice with occult tumors from control group (area under curve (AUC) of 0.935 (training data) and 0.912 (testing data)) (gene signature including Cd19, Cdkn1a, S100a9, Tap1, and Tnfrsf1b) and also from macroscopic tumor group (AUC of 0.920 (training data) and 0.936 (testing data)) (gene signature including Ccr7, Cd4, Kmt2d, and Ly6e). CONCLUSIONS: Our proof-of-concept study provides evidence for potential clinical relevance of blood platelets as a platform for liquid biopsy-based early detection of cancer.

Characterization of biosurfactant lipopeptide and its performance evaluation for oil-spill remediation.

Biosurfactant lipopeptide is a promising dispersant over varieties of chemical ones in oil-spill remediation. The toxicity, biodegradability and performance of the biosurfactant lipopeptide are studied in this paper.

Characterization of bacterial composition and diversity in a long-term petroleum contaminated soil and isolation of high-efficiency alkane-degrading strains using an improved medium.

Bacterial community and diversity in a long-term petroleum-contaminated soil of an oilfield were characterized using 16S rRNA gene-based Illumina MiSeq high-throughput sequencing. Results indicated that Proteobacteria (49.11%) and Actinobacteria (24.24%) were the most dominant phyla, and the most abundant genera were Pseudoxanthomonas (8.47%), Luteimonas (3.64%), Alkanindiges (9.76%), Acinetobacter (5.26%) and Agromyces (8.56%) in the soil. Meanwhile a series of cultivations were carried out for isolation of alkane degraders from petroleum-contaminated soil with gellan gum and agar as gelling agents. And the isolates were classified by their 16S rRNA genes. Nine of the isolates including Enterobacter, Pseudomonas,Acinetobacter, Rhizobium, Bacillus, Sphingomonas, Paenibacillus, Variovorax and Rhodococcus showed strong biodegradability of alkane mixture (C9-C30) in a wide range of chain-length, which could be potentially applied in enhancement of bioremediation.