15N tracing reveals preference for different nitrogen forms of Fusarium oxysporum f. sp. cubense tropical race 4.

Plant uptake of nitrogen is often associated with increased incidence of banana Fusarium wilt, a disease caused by the soil-borne fungus Fusarium oxysporum f. sp. cubense tropical race 4 (Foc TR4). However, the nitrogen metabolic preferences of Foc TR4 pathogens remain unknown. In this study, we investigated the ecophysiological patterns of Foc TR4 grown on different combinations of organic and inorganic nitrogen. Potato Dextrose Agar (PDA) and Rose Bengal Medium (RBM) were used as an organic nitrogen source, which was sequentially replaced with inorganic N (0, 50% or 90%) in the form 15NH4NO3 or NH4 15NO3 to reveal preferential assimilation of ammonium or nitrate. The results showed that mycelium biomass and nitrogen content decreased significantly, while the carbon content and C:N ratio increased in Foc TR4 grown on media containing inorganic nitrogen sources. Mycelium biomass was negatively correlated with C:N ratio. Mycelium 15N abundance increased significantly between the PDA50 + A50/RBM50 + A50 treatments (50% organic nitrogen+50%15NH4NO3) and the PDA10 + A90/RBM10 + A90 treatments (10% organic nitrogen+90%15NH4NO3). These results indicate that the higher C:N ratio reduced mycelium growth by reducing its biomass and diameter and showed that Foc TR4 preferred to use ammonium nitrogen to promote the growth. These findings suggest that treating banana crops with a combination of organic and inorganic (i.e., nitrate) nitrogen could be a better way to defend against Fusarium wilt of banana.

Rice straw increases microbial nitrogen fixation, bacterial and nifH genes abundance with the change of land use types.

Soil microorganisms play an important role in soil ecosystems as the main decomposers of carbon and nitrogen. They have an indispensable impact on soil health, and any alterations in the levels of organic carbon and inorganic nitrogen can significantly affect soil chemical properties and microbial community composition. Previous studies have focused on the effects of carbon and nitrogen addition on a single type of soil, but the response of soil microorganisms to varying carbon and nitrogen inputs under different land soil use types have been relatively understudied, leaving a gap in our understanding of the key influencing factors. To address this gap, we conducted a study in the tropical regions of Hainan province, focusing on four distinct land use types: natural forest soil (NS), healthy banana soil (HS), diseased banana garden soil (DS), and paddy soil (PS). Within each of these environments, we implemented five treatments: CK, RS (rice straw), RSN (rice straw and NH4NO3), RR (rice root), and RRN (rice root and NH4NO3). Our aim was to investigate how soil bacteria response to changes in carbon and nitrogen inputs, and to assess their potential for biological nitrogen fixation. The results showed that the addition of rice straw increased the absorption and utilization of nitrate nitrogen by microorganisms. The addition of rice roots (RR) did not increase the absorption capacity of inorganic nitrogen by microorganisms, but increased the content of poorly soluble organic carbon. Most importantly, the addition of rice straw increased microbial respiration and the utilization efficiency of N2 by microorganisms, and the further addition of ammonium nitrate increased microbial respiration intensity. With the change of soil type, the rice straw increases microbial nitrogen fixation, bacterial and nifH genes abundance. Meanwhile, microbial respiration intensity is an important factor influencing the differences in the structure of bacterial communities. The addition of inorganic nitrogen resulted in ammonium nitrogen accumulation, reduced microbial richness and diversity, consequently diminishing the soil microorganisms to resist the environment. Therefore, we believe that with the change of soil types, corresponding soil nutrient retention strategies should be devised and incorporated while reducing the application of ammonium nitrogen, thus ensuring healthy soil development.

Diagnostic Value of Inflammatory Markers and Cytokines in Neonatal Sepsis.

Objective: To explore the diagnosis value of inflammatory markers and cytokines in neonatal sepsis. Methods: In this retrospective analysis, 90 cases of neonatal sepsis admitted to our hospital from April 2019 to April 2021 were included in the observation group, and 70 healthy neonates who received routine physical examinations in our hospital during the same period were recruited as the control group. Comparison and analysis of inflammatory markers and cytokines levels between the two groups were performed on days 1, 3, and 7 after the onset. Flow cytometry was used to measure the white blood cells (WBCs) and percentage of neutrophils (N%), immunoturbidimetry was used to determine C-reactive protein (CRP), immunochromatographic analysis was used to determine procalcitonin (PCT) in plasma, and the enzyme-linked immunosorbent assay was used to determine interleukin-27 (IL-27), interleukin-6 (IL-6), interleukin-10 (IL-10), and tumor necrosis factor-α (TNF-α). Results: Compared with healthy controls, neonatal sepsis resulted in significantly higher levels of WBC, N%, PCT, and CRP on days 1, 3, and 7 after onset. The levels of WBC, N%, and PCT were continuously decreased from day 1 to day 7, while the levels of CRP were increased on day 1 and day 3 but declined on day 7 (P < 0.05). Compared with healthy controls, patients with sepsis showed higher levels of IL-27, IL-6, IL-10, and TNF-α on days 1, 3, and 7 after the onset. The levels of IL-27, IL-6, and IL-10 were increased on day 1 and day 3 but decreased on day 7, and the levels of TNF-α were continuously decreased from day 1 to day 7 (all P < 0.05). Conclusion: Neonatal sepsis was associated with fluctuating levels of WBC, N%, PCT, CRP, IL-27, IL-6, IL-10, and TNF-α at different time points of disease. The joint detection of the above indices provides a new pathway for the early diagnosis of neonatal sepsis.

Systematic identification and expression analysis of the Sox gene family in spotted sea bass (Lateolabrax maculatus).

The Sox gene family encodes a set of transcription factors characterized by a conserved Sry-related high mobility group (HMG)-box domain, which performs a series of essential biological functions in diverse tissues and developmental processes. In this study, the Sox gene family was systematically characterized in spotted sea bass (Lateolabrax maculatus). A total of 26 Sox genes were identified and classified into eight subfamilies, namely, SoxB1, SoxB2, SoxC, SoxD, SoxE, SoxF, SoxH and SoxK. The phylogenetic relationship, exon-intron and domain structure analyses supported their annotation and classification. Comparison of gene copy numbers and chromosome locations among different species indicated that except tandem duplicated paralogs of Sox17/Sox32, duplicated Sox genes in spotted sea bass were generated from teleost-specific whole genome duplication during evolution. In addition, qRT-PCR was performed to detect the expression profiles of Sox genes during development and adulthood. The results showed that the expression of 16 out of 26 Sox genes was induced dramatically at different starting points after the multicellular stage, which is consistent with embryogenesis. At the early stage of sex differentiation, 9 Sox genes exhibited sexually dimorphic expression patterns, among which Sox3, Sox19 and Sox6b showed the most significant ovary-biased expression. Moreover, the distinct expression pattern of Sox genes was observed in different adult tissues. Our results provide a fundamental resource for further investigating the functions of Sox genes in embryonic processes, sex determination and differentiation as well as controlling the homeostasis of adult tissues in spotted sea bass.

CNN-Based Volume Flow Rate Prediction of Oil-Gas-Water Three-Phase Intermittent Flow from Multiple Sensors.

In this paper, we propose a deep-learning-based method using a convolutional neural network (CNN) to predict the volume flow rates of individual phases in the oil-gas-water three-phase intermittent flow simultaneously by analyzing the measurement data from multiple sensors, including a temperature sensor, a pressure sensor, a Venturi tube and a microwave sensor. To build datasets, a series of experiments for the oil-gas-water three-phase intermittent flow in a horizontal pipe, in which gas volume fraction and water-in-liquid ratio ranges are 23.77-94.45% and 14.95-86.97%, respectively, and gas flow superficial velocity and liquid flow superficial velocity ranges are 0.66-5.23 and 0.27-2.14 m/s, respectively, have been carried out on a test loop pipeline. The preliminary results indicate that the model can provide relative prediction errors on the testing-1 dataset for the volume flow rates of oil-phase, gas-phase and water-phase within ±10% with 94.49%, 92.56% and 95.71% confidence levels, respectively. Additionally, the prediction results on the testing-2 dataset also demonstrate the generalization ability of the model. The consuming time of a prediction with one sample is 0.43 s on an Intel Xeon CPU E5-2678 v3, and 0.01 s on an NVIDIA GeForce GTX 1080 Ti GPU. Hence, the proposed CNN-based prediction model, which can fulfill the real-time application requirements in the petroleum industry, reveals the potential of using deep learning to obtain accurate results in the multiphase flow measurement field.

Flow Adversarial Networks: Flowrate Prediction for Gas-Liquid Multiphase Flows Across Different Domains.

The solution of how to accurately and timely predict the flowrate of gas-liquid mixtures is the key to help petroleum and other related industries to reduce costs, improve efficiency, and optimize management. Although numerous studies have been carried out over the past decades, the problem is still significantly challenging due to the complexity of multiphase flows. This paper attempts to seek new possibilities for multiphase flow measurement and novel application scenarios for state-of-the-art machine learning (ML) techniques. Convolutional neural networks (CNNs) are applied to predict the flowrate of multiphase flows for the first time and can achieve promising performance. In addition, considering the difference between data distributions of training and testing samples and its negative impact on prediction accuracy of the CNN models on testing samples, we propose flow adversarial networks (FANs) that can distill both domain-invariant and flowrate-discriminative features from the raw input. The method is evaluated on dynamic experimental data of different multiphase flows on different flow conditions and operating environments. The experimental results demonstrate that FANs can effectively prevent the accuracy degradation caused by the gap between training and testing samples and have better performance than state-of-the-art approaches in the flowrate prediction field.

Isolation and expression analysis of a candidate gametophyte sex determination gene (sjhmg) of kelp (Saccharina japonica).

Saccharina japonica undergoes an alternating life cycle during which the diploid sporophyte generation alternates with the happloid gametophyte generation. Saccharina japonica uses the UV sex determination system to determine the sex of its haploid gametophytes. However, the sex-determining genes and the sex-determining mechanisms of kelp gametophytes have not been thoroughly elucidated to date. In this study, a kelp HMG-box-containing gene (SjHMG), which is located within the sex determination region of S. japonica, was isolated and characterized. SjHMG contained an open reading frame of 1,266 bp in length and encoded a deduced protein of 421 amino acid residues with two HMG-box domains. Phylogenetic analysis showed the strongest relationship between SjHMG and its orthologs in brown algae. An alternatively spliced transcript (SjHMG isoform-2) encoding a protein of 256 amino acid residues was also identified. The two isoforms were specific for male gametophytes. A real-time quantitative PCR analysis showed significantly higher abundances of two isoforms in immature male gametophytes than in mature ones. These findings suggested that the SjHMG gene is a candidate male gametophyte determination gene of kelp. In addition, the abundance of SjHMG isoform-2 transcripts was significantly lower than that of SjHMG isoform-1 transcripts, and only an HMG-box domain was conserved among species in the order Laminariales, which indicated that the gene is specifically involved in sex regulation in some species of the order Laminariales by alternative splicing.

A Benchmark Dataset and Deep Learning-Based Image Reconstruction for Electrical Capacitance Tomography.

Electrical Capacitance Tomography (ECT) image reconstruction has developed for decades and made great achievements, but there is still a need to find a new theoretical framework to make it better and faster. In recent years, machine learning theory has been introduced in the ECT area to solve the image reconstruction problem. However, there is still no public benchmark dataset in the ECT field for the training and testing of machine learning-based image reconstruction algorithms. On the other hand, a public benchmark dataset can provide a standard framework to evaluate and compare the results of different image reconstruction methods. In this paper, a benchmark dataset for ECT image reconstruction is presented. Like the great contribution of ImageNet that transformed machine learning research, this benchmark dataset is hoped to be helpful for society to investigate new image reconstruction algorithms since the relationship between permittivity distribution and capacitance can be better mapped. In addition, different machine learning-based image reconstruction algorithms can be trained and tested by the unified dataset, and the results can be evaluated and compared under the same standard, thus, making the ECT image reconstruction study more open and causing a breakthrough.

LRG1 modulates invasion and migration of glioma cell lines through TGF-ß signaling pathway.

Studies have shown that the abnormal expression of leucine-rich α2 glycoprotein 1 (LRG1) is associated with multiple malignancies, yet its role in glioma pathology remains to be elucidated. In this study, we investigated the role of LRG1 in regulating proliferation, migration and invasion of glioma cells by establishing glioma cell strains with constitutively silenced or elevated LRG1 expression. LRG1 overexpression and silenced cell lines demonstrated modulation of glioma cellular proliferation, migration and invasion through MTT, cell scratching and Transwell assays. Furthermore, overexpression of LRG1 led to augmented activation of transforming growth factor-ß (TGF-ß) signaling pathway as well as downregulation of E-cadherin and resultant enhanced invasiveness, which was reversed by TGF-ß signaling pathway inhibitor SB431542. In summary, our findings suggest that LRG1 promotes invasion and migration of glioma cells through TGF-ß signaling pathway.

Stable knockdown of LRG1 by RNA interference inhibits growth and promotes apoptosis of glioblastoma cells in vitro and in vivo.

Leucine-rich α2 glycoprotein 1 (LRG1) has been shown to be aberrantly expressed in multiple human malignancies. However, the biological functions of LRG1 in human glioblastoma remain unknown. Here, we report for the first time the role of LRG1 in glioblastoma development based on the preliminary in vitro and in vivo data. We first confirmed the expression of LRG1 in human glioblastoma cell lines. Next, to investigate the role of LRG1 in the tumorigenesis and development of glioblastoma, a short hairpin RNA (shRNA) construct targeting LRG1 mRNA was transfected into U251 glioblastoma cells to generate a cell line with stably silenced LRG1 expression. The results showed that silencing of LRG1 significantly inhibited cell proliferation, induced cell cycle arrest at G0/G1 phase, and enhanced apoptosis in U251 cells in vitro. Consistently, LRG1 silencing resulted in the downregulation of key cell cycle factors including cyclin D1, B, and E and apoptotic gene Bcl-2 while elevated the levels of pro-apoptotic Bax and cleaved caspase-3, as determined by Western blot analysis. We further demonstrate that the silencing of LRG1 expression effectively reduced the tumorigenicity of U251 cells, delayed tumor formation, and promoted apoptosis in a xenograft tumor model in vivo. In conclusion, silencing the expression of LRG1 suppresses the growth of glioblastoma U251 cells in vitro and in vivo, suggesting that LRG1 may play a critical role in glioblastoma development, and it may have potential clinical implications in glioblastoma therapy.