Additional file 1 of Understanding and reconstructing the coastal sea level variations along the western boundary of the North Pacific

Additional file 1: Tables S1–S6. The same regression conducted as in Table 2. Five out of all the tide gauges south of the KE are selected randomly to estimate the coefficients. Figure S1. The time series of SLA of the three tide gauges south of the KE used for evaluation and the gray lines are the SLAs of the tide gauges used to build the equations. All tide gauge data is 1-year low passed. Figure S2. The same as Fig. S1, but for north of the KE. Figure S3. The time series of SL of tide gauges north of the KE

Supplemental Material - Development and evaluation of a risk score model based on a WNT score gene-associated signature for predicting the clinical outcome and the tumour microenvironment of hepatocellular carcinoma

Supplemental Material for Development and evaluation of a risk score model based on a WNT score gene-associated signature for predicting the clinical outcome and the tumour microenvironment of hepatocellular carcinoma by Penghui Li, Xiao Ma, Di Huang, and Xinyu Gu in International Journal of Immunopathology and Pharmacology</p

Relationship between salinity and ratios of F_max of humic-like components to CDOM absorption coefficient for four cruises.

<p>Relationship between salinity and ratios of F_max of humic-like components to CDOM absorption coefficient for four cruises.</p

Exploring plant metabolic genomics: chemical diversity, metabolic complexity in the biosynthesis and transport of specialized metabolites with the tea plant as a model

The diversity and complexity of secondary metabolites in tea plants contribute substantially to the popularity of tea, by determining tea flavors and their numerous health benefits. The most significant characteristics of tea plants are that they concentrate the complex plant secondary metabolites into one leaf: flavonoids, alkaloids, theanine, volatiles, and saponins. Many fundamental questions regarding tea plant secondary metabolism remain unanswered. This includes how tea plants accumulate high levels of monomeric galloylated catechins, unlike the polymerized flavan-3-ols in most other plants, as well as how they are evolved to selectively synthesize theanine and caffeine, and how tea plants properly transport and store these cytotoxic products and then reuse them in defense. Tea plants coordinate many metabolic pathways that simultaneously take place in young tea leaves in response to both developmental and environmental cues. With the available genome sequences of tea plants and high-throughput metabolomic tools as great platforms, it is of particular interest to launch metabolic genomics studies using tea plants as a model system. Plant metabolic genomics are to investigate all aspects of plant secondary metabolism at the genetic, genome, and molecular levels. This includes plant domestication and adaptation, divergence and convergence of secondary metaboloic pathways. The biosynthesis, transport, storage, and transcriptional regulation mechanisms of all metabolites are of core interest in the plant as a whole. This review highlights relevant contexts of metabolic genomics, outstanding questions, and strategies for answering them, with aim to guide future research for genetic improvement of nutrition quality for healthier plant foods.</p

Spatiotemporal Distribution, Sources, and Photobleaching Imprint of Dissolved Organic Matter in the Yangtze Estuary and Its Adjacent Sea Using Fluorescence and Parallel Factor Analysis

<div><p>To investigate the seasonal and interannual dynamics of dissolved organic matter (DOM) in the Yangtze Estuary, surface and bottom water samples in the Yangtze Estuary and its adjacent sea were collected and characterized using fluorescence excitation-emission matrices (EEMs) and parallel factor analysis (PARAFAC) in both dry and wet seasons in 2012 and 2013. Two protein-like components and three humic-like components were identified. Three humic-like components decreased linearly with increasing salinity (<i>r</i>>0.90, <i>p</i><0.001), suggesting their distribution could primarily be controlled by physical mixing. By contrast, two protein-like components fell below the theoretical mixing line, largely due to microbial degradation and removal during mixing. Higher concentrations of humic-like components found in 2012 could be attributed to higher freshwater discharge relative to 2013. There was a lack of systematic patterns for three humic-like components between seasons and years, probably due to variations of other factors such as sources and characteristics. Highest concentrations of fluorescent components, observed in estuarine turbidity maximum (ETM) region, could be attributed to sediment resuspension and subsequent release of DOM, supported by higher concentrations of fluorescent components in bottom water than in surface water at two stations where sediments probably resuspended. Meanwhile, photobleaching could be reflected from the changes in the ratios between fluorescence intensity (F_max) of humic-like components and chromophoric DOM (CDOM) absorption coefficient (a355) along the salinity gradient. This study demonstrates the abundance and composition of DOM in estuaries are controlled not only by hydrological conditions, but also by its sources, characteristics and related estuarine biogeochemical processes.</p></div

Relative differences between surface and bottom FDOM concentrations along the axial section of the Yangtze Estuary in 2013.

<p>The left is for section E in July 2013, and the right is for section F in March 2013. ΔC is calculated as the component concentration in bottom water subtracting the component concentration in surface water. Positive ΔC means a higher component concentration in bottom water than that in surface water.</p

Data_Sheet_1_Transformation of farmland use and driving mechanism in Xinjiang since China’s Western Development Policy.docx

Since the implementation of China’s Western Development Policy, Xinjiang has experienced rapid socio-economic development and significant changes in its land use patterns. As an important factor in agricultural production, farmland is of crucial for realizing the rural revitalization strategy. Based the theoretical mechanisms of farmland use transformation, this study selected five periods of land use and socioeconomic data from 2000, 2005, 2010, 2015, and 2018 to study the spatial and temporal evolutionary characteristics of farmland use transformation in Xinjiang since China’s Western Development Policy. We then explored the driving mechanisms using an optimal geographic detector model based on parameters. The results showed that (1) Xinjiang’s farmland use transitioned toward large scale and multifunctionality, and the transition characteristics are mainly of fluctuating growth type. The spatial transformation and functional transformation characteristics were generally consistent in spatial distribution. (2) There was a spatial agglomeration in the transformation, which was concentrated in the economic zone of the northern slope of Tianshan Mountain, the Yili River Valley and Kashgar region. The concentration of functional transformation of farmland has increased, but the spatial transformation of farmland has weakened. (3) The role of influencing factors on the transformation of farmland use differed with periods. Finally, the study concluded that the functional transformation of farmland in Xinjiang since China’s Western Development Policy is still at the stage of mainly production function. We suggest that the protection of farmland in Xinjiang in the New Western Development period should be achieved by promoting the transformation of the function of farmland. The findings of this study provide decision-making assistance for the management of farmland use in Xinjiang during the New Western Development period and are an effective tool for achieving the goals of sustainable farmland use and agricultural and rural modernization.</p

Map of sampling stations in four different cruises.

<p>(a) March 2012; (b) July 2012; (c) March 2013; (d) July 2013. Four axial sections of the Yangtze Estuary are connected and marked by red lines. ETM regions are marked by dashed eclipses. Black dots indicate the sampling sites.</p

Table2_Heterogeneity characterization of hepatocellular carcinoma based on the sensitivity to 5-fluorouracil and development of a prognostic regression model.DOCX

Background: 5-Fluorouracil (5-FU) is a widely used chemotherapeutic drug in clinical cancer treatment, including hepatocellular carcinoma (HCC). A correct understanding of the mechanisms leading to a low or lack of sensitivity of HCC to 5-FU-based treatment is a key element in the current personalized medical treatment.Methods: Weighted gene co-expression network analysis (WGCNA) was used to analyze the expression profiles of the cancer cell line from GDSC2 to identify 5-FU-related modules and hub genes. According to hub genes, HCC was classified and the machine learning model was developed by ConsensusClusterPlus and five different machine learning algorithms. Furthermore, we performed quantitative reverse transcription-polymerase chain reaction (qRT-PCR) analysis on the genes in our model.Results: A total of 19 modules of the cancer cell line were divided by WGCNA, and the most negative correlation with 5-FU was the midnight blue module, from which 45 hub genes were identified. HCC was divided into three subgroups (C1, C2, and C3) with significant overall survival (OS) differences. OS of C1 was the shortest, which was characterized by a high clinical grade and later T stage and stage. OS of C3 was the longest. OS of C2 was between the two subtypes, and its immune infiltration was the lowest. Five out of 45 hub genes, namely, TOMM40L, SNRPA, ILF3, CPSF6, and NUP205, were filtered to develop a risk regression model as an independent prognostic indicator for HCC. The qRT-PCR results showed that TOMM40L, SNRPA, ILF3, CPSF6, and NUP205 were remarkably highly expressed in hepatocellular carcinoma.Conclusion: The HCC classification based on the sensitivity to 5-FU was in line with the prognostic differences observed in HCC and most of the genomic variation, immune infiltration, and heterogeneity of pathological pathways. The regression model related to 5-FU sensitivity may be of significance in individualized prognostic monitoring of HCC.</p

Variations of five fluorescent components in surface water samples along the axial section of the Yangtze Estuary.

<p>ETM regions are indicated by green areas.</p