Distribution patterns of plant communities and their associations with environmental soil factors on the eastern shore of Lake Taihu, China

Introduction: Plant communities and soil factors might interact with each other in different temporal and spatial scales, which can influence the patterns and processes of the wetland ecosystem. To get a better understanding of the distribution of plants in wetlands and analyze their associations with environmental soil factors, the structure and types of plant communities in the eastern shore area of Lake Taihu were analyzed by two-way indicator species analysis and canonical correspondence analysis (CCA) ordination. The spatial distribution patterns of vegetation and the main factors affecting the distributions were investigated.Outcomes: Sixty-six sampling sites were selected to obtain vegetation species and soil environmental factor data. Results showed that 22 species from the 66 sites could be divided into seven communities: I: Arundo donax; II: A. donax + Phragmites australis; III: Zizania latifolia + Typha orientalis; IV: P. australis + Alternanthera philoxeroides + Polygonum hydropiper; V: P. australis; VI: P. australis + Humulus scandens; and VII: Erigeron acer + Ipomoea batatas + Rumex acetosa. Plant species and soil factors in the CCA analysis showed that I. batatas, E. acer, Chenopodium album, Polygonum lapathifolium, and Acalypha australis were mainly affected by pH, whereas Echinochloa crus-galli, Setaria viridis, and H. scandens were mainly affected by soil total phosphorus. Mentha canadensis and A. donax were mainly affected by soil conductivity, A. philoxeroides was mainly affected by soil organic matter and, Z. latifolia, Metaplexis japonica and P. hydropiper were mainly affected by available phosphorus.Conclusion:These results indicated that different plants adapted to different soil environmental factors and provided basic information on the diversity of Lake Taihu wetland vegetation

Addressing the Modelling Precision in Evaluating the Ecosystem Services of Coastal Wetlands

Wetlands are one of the world’s most productive ecosystems, and therefore it is crucial that management decisions regarding wetlands incorporate awareness of accurate assessments of the value of their respective ecosystem services. In this paper, we seek to improve the modelling precision in the scale transform process of ecosystem service evaluation. Firstly, we selected eight services as the criteria to calculate wetland ecosystem values: substance production, flood control, carbon sequestration, gas regulation, climate regulation, wave reduction, adding new lands, recreation and education. Then, six coastal wetlands of Liaoning province were chosen as the case study areas, and their ecosystem values were calculated by empirical method. Next, we simulated ecosystem values of the six cases by two spatial-scales transform methods named meta-analysis and wavelet transform. Finally, we compared the two groups of simulated values with the empirical measured values to examine their evaluation precisions. The results indicated that the total precision of the wavelet transform model (0.968) was higher than that of meta-analysis (0.712). In addition, the simulated values of single services such as substance production, flood control, carbon sequestration, gas regulation, and climate regulation were closer to the measured values using wavelet transform model. This research contributes to identifying an evaluation model with higher precision for evaluating wetland ecosystem services in the process of scale transform

Tet1 Loss Cooperates with AML1-ETO to Promote Leukemogenesis in Mice

DNA methylation is a defining feature with widespread involvement in many biological processes. Recent studies showed that dysregulated DNA methylation not only involves pathological processes and development, but also demonstrates dynamic feature in both locus-specific and genetic lesions-specific manner. AML1-ETO, a fusion protein encoded by t(8;21), one of the most frequent karyotype abnormalities in Acute Myeloid Leukemia (AML), was observed in proximately 10%~15% of human AMLs, and partially served as a diagnostic criterion for M2 subtype. However, AML1-ETO itself is not sufficient in promoting leukemia. In this study, we generated an in vivomouse model harboring both AML1-ETOconditional knockin and Tet1conditional knockout and observed a synergistic effect in leukemogenesis. Specifically, short survival was observed in mice with both Tet1loss and AML1-ETOexpression, with accelerated pathogenesis of myeloid malignancies, including myeloid leukemia with maturation, MPD-like leukemia and erythroid leukemia. The significance of this study lies in the discovery of synergistic effect between dysregulated DNA methylation mediated by Tet1loss and the expression of AML1-ETO fusion protein, paving the way for further mechanistic study and providing an in vivomodel for the testing of therapeutic agents.</p

Dictionary Learning for Few-Shot Remote Sensing Scene Classification

With deep learning-based methods growing (even with scarce data in some fields), few-shot remote sensing scene classification (FSRSSC) has received a lot of attention. One mainstream approach uses base data to train a feature extractor (FE) in the pre-training phase and employs novel data to design the classifier and complete the classification task in the meta-test phase. Due to the scarcity of remote sensing data, obtaining a suitable feature extractor for remote sensing data and designing a robust classifier have become two major challenges. In this paper, we propose a novel dictionary learning (DL) algorithm for few-shot remote sensing scene classification to address these two difficulties. First, we use natural image datasets with sufficient data to obtain a pre-trained feature extractor. We fine-tune the parameters with the remote sensing dataset to make the feature extractor suitable for remote sensing data. Second, we design the kernel space classifier to map the features to a high-dimensional space and embed the label information into the dictionary learning to improve the discrimination of features for classification. Extensive experiments on four popular remote sensing scene classification datasets demonstrate the effectiveness of our proposed dictionary learning method

A Long Noncoding RNA Perturbs the Circadian Rhythm of Hepatoma Cells to Facilitate Hepatocarcinogenesis

Clock circadian regulator (CLOCK)/brain and muscle arnt-like protein-1 (BMAL1) complex governs the regulation of circadian rhythm through triggering periodic alterations of gene expression. However, the underlying mechanism of circadian clock disruption in hepatocellular carcinoma (HCC) remains unclear. Here, we report that a long noncoding RNA (lncRNA), highly upregulated in liver cancer (HULC), contributes to the perturbations in circadian rhythm of hepatoma cells. Our observations showed that HULC was able to heighten the expression levels of CLOCK and its downstream circadian oscillators, such as period circadian clock 1 and cryptochrome circadian clock 1, in hepatoma cells. Strikingly, HULC altered the expression pattern and prolonged the periodic expression of CLOCK in hepatoma cells. Mechanistically, the complementary base pairing between HULC and the 5' untranslated region of CLOCK mRNA underlay the HULC-modulated expression of CLOCK, and the mutants in the complementary region failed to achieve the event. Moreover, immunohistochemistry staining and quantitative real-time polymerase chain reaction validated that the levels of CLOCK were elevated in HCC tissues, and the expression levels of HULC were positively associated with those of CLOCK in clinical HCC samples. In functional experiments, our data exhibited that CLOCK was implicated in the HULC-accelerated proliferation of hepatoma cells in vitro and in vivo. Taken together, our data show that an lncRNA, HULC, is responsible for the perturbations in circadian rhythm through upregulating circadian oscillator CLOCK in hepatoma cells, resulting in the promotion of hepatocarcinogenesis. Thus, our finding provides new insights into the mechanism by which lncRNA accelerates hepatocarcinogenesis through disturbing circadian rhythm of HCC

Dictionary Learning for Few-Shot Remote Sensing Scene Classification

With deep learning-based methods growing (even with scarce data in some fields), few-shot remote sensing scene classification (FSRSSC) has received a lot of attention. One mainstream approach uses base data to train a feature extractor (FE) in the pre-training phase and employs novel data to design the classifier and complete the classification task in the meta-test phase. Due to the scarcity of remote sensing data, obtaining a suitable feature extractor for remote sensing data and designing a robust classifier have become two major challenges. In this paper, we propose a novel dictionary learning (DL) algorithm for few-shot remote sensing scene classification to address these two difficulties. First, we use natural image datasets with sufficient data to obtain a pre-trained feature extractor. We fine-tune the parameters with the remote sensing dataset to make the feature extractor suitable for remote sensing data. Second, we design the kernel space classifier to map the features to a high-dimensional space and embed the label information into the dictionary learning to improve the discrimination of features for classification. Extensive experiments on four popular remote sensing scene classification datasets demonstrate the effectiveness of our proposed dictionary learning method

The Effect of Low Irradiance on Leaf Nitrogen Allocation and Mesophyll Conductance to CO2 in Seedlings of Four Tree Species in Subtropical China

Low light intensity can lead to a decrease in photosynthetic capacity. However, could N-fixing species with higher leaf N contents mitigate the effects of low light? Here, we exposed seedlings of Dalbergia odorifera and Erythrophleum fordii (N-fixing trees), and Castanopsis hystrix and Betula alnoides (non-N-fixing trees) to three irradiance treatments (100%, 40%, and 10% sunlight) to investigate the effects of low irradiance on leaf structure, leaf N allocation strategy, and photosynthetic physiological parameters in the seedlings. Low irradiance decreased the leaf mass per unit area, leaf N content per unit area (Narea), maximum carboxylation rate (Vcmax), maximum electron transport rate (Jmax), light compensation point, and light saturation point, and increased the N allocation proportion of light-harvesting components in all species. The studied tree seedlings changed their leaf structures, leaf N allocation strategy, and photosynthetic physiological parameters to adapt to low-light environments. N-fixing plants had a higher photosynthesis rate, Narea, Vcmax, and Jmax than non-N-fixing species under low irradiance and had a greater advantage in maintaining their photosynthetic rate under low-radiation conditions, such as under an understory canopy, in a forest gap, or when mixed with other species

Seedling leaves allocate lower fractions of nitrogen to photosynthetic apparatus in nitrogen fixing trees than in non-nitrogen fixing trees in subtropical China.

Photosynthetic-nitrogen use efficiency (PNUE) is a useful trait to characterize leaf physiology and survival strategy. PNUE can also be considered as part of 'leaf economics spectrum' interrelated with leaf nutrient concentrations, photosynthesis and respiration, leaf life-span and dry-mass investment. However, few studies have paid attention to PNUE of N-fixing tree seedlings in subtropical China. In this study, we investigated the differences in PNUE, leaf nitrogen (N) allocation, and mesophyll conductance (gm) in Dalbergia odorifera and Erythrophleum fordii (N-fixing trees), and Betula alnoides and Castanopsis hystrix (non-N-fixing trees). PNUE of D. odorifera and E. fordii were significantly lower than those of B. alnoides and C. hystrix mainly because of their allocation of a lower fraction of leaf N to Rubisco (PR) and bioenergetics (PB). Mesophyll conductance had a significant positive correlation with PNUE in D. odorifera, E. fordii, and B. alnoides, but the effect of gm on PNUE was different between species. The fraction of leaf N to cell wall (PCW) had a significant negative correlation with PR in B. alnoides and C. hystrix seedling leaves, but no correlation in D. odorifera and E. fordii seedling leaves, which may indicate that B. alnoides and C. hystrix seedling leaves did not have enough N to satisfy the demand from both the cell wall and Rubisco. Our results indicate that B. alnoides and C. hystrix may have a higher competitive ability in natural ecosystems with fertile soil, and D. odorifera and E. fordii may grow well in N-poor soil. Mixing these non-N-fixing and N-fixing trees for afforestation is useful for improving soil N utilization efficiency in the tropical forests