Expression and Significance of PKM1 in Acute Myeloid Leukaemia.

OBJECTIVE: To investigate the expression level of pyruvate kinase M1 (PKM1) in patients with acute myeloid leukaemia (AML) as well as its clinical significance. STUDY DESIGN: A case-control study. Place and Duration of the Study: Department of Haematology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China, from January 2013 to 2023. METHODOLOGY: The expression levels of PKM1 and pyruvate kinase m2 (PKM2) in the bone marrow of 65 AML patients (excluding M3) and 31 healthy volunteers were determined using reverse transcription-quantitative polymerase chain reaction (RT-qPCR), a method that measures fluorescence in real-time. The associations between PKM1, PKM2 expressions, clinical parameters, and the survival and prognosis of AML patients were analysed. RESULTS: AML patients showed higher PKM1 expression compared to controls. The area under the curve (AUC) of the receiver operating characteristics (ROC) was 0.65 (p = 0.017). PKM1 expression was correlated with peripheral blood leukocyte count (r = -0.276, p = 0.026), CCAAT enhancer-binding protein alpha CEBPA mutation (r = -0.306, p = 0.014), and chemotherapy-induced response (r = -0.292, p = 0.018). Patients with high PKM1 expression had a lower remission rate (p = 0.019) and long-term survival rate (p = 0.034) than those with low PKM1 expression. Patients with AML showed a rise in PKM2 levels; however, the variation was not statistically significant (p >0.05). CONCLUSION: PKM1 expression is upregulated in AML and patients with high PKM1 expression have a lower survival rate. KEY WORDS: PKM1, Acute myeloid leukaemia, Clinical prognosis.

Revealing the spatiotemporal patterns of water vapor and its link to North Atlantic Oscillation over Greenland using GPS and ERA5 data.

Precipitation plays an important role in the interannual mass variations of Greenland Ice Sheet (GrIS) and is highly influenced by atmospheric circulation change. The relationship between precipitation and North Atlantic Oscillation (NAO) has been revealed by many studies, but the role of water vapor transportation in the NAO-precipitation relationship was rarely investigated. Therefore, to fill the knowledge gap of how water vapor changes and responds to NAO in space and time, we applied Multichannel Singular Spectral Analysis (MSSA) to the Global Positioning System (GPS) and the fifth-generation reanalysis dataset of the European Center for Medium-Range Weather Forecasting (ERA5) Precipitable Water Vapor (PWV) data to extract the interannual PWV signals in Greenland. Results show that the interannual PWV signals overall increased in 2008-2011, decreased in 2011-2015, and increased in 2015-2021. The amplitudes of the interannual signals derived from both the GPS PWV and ERA5 basin-averaged PWV exhibited an overall southwest-northeast decreasing gradient. We also found anticorrelation between the interannual PWV signals and the NAO signal over Greenland but the correlation coefficients are not statistically significant, and the correlation coefficients in most cases were less than -0.65, indicating that positive (negative) NAO phase decreased (increased) the water vapor content. The Fast Fourier Transform (FFT) results illustrated that the interannual signals derived from both the GPS site-dependent and the ERA5 basin-averaged PWV had similar dominant frequencies to that of the NAO signal, reinforcing their correlations. This study reveals the spatiotemporal pattern of the interannual water vapor and its linkage to the NAO, providing a new perspective for understanding the climate change on Greenland.

A multi-institutional study to predict the benefits of DEB-TACE and molecular targeted agent sequential therapy in unresectable hepatocellular carcinoma using a radiological-clinical nomogram.

OBJECTIVE: Exploring the efficacy of a Radiological-Clinical (Rad-Clinical) model in predicting prognosis of unresectable hepatocellular carcinoma (HCC) patients after drug eluting beads transcatheter arterial chemoembolization (DEB-TACE) to optimize the targeted sequential treatment. METHODS: In this retrospective analysis, we included 202 patients with unresectable HCC who received DEB-TACE treatment in 17 institutions from June 2018 to December 2022. Progression-free survival (PFS)-related radiomics features were computationally extracted from HCC patients to build a radiological signature (Rad-signature) model with least absolute shrinkage and selection operator regression. A Rad-Clinical model for postoperative PFS was further constructed according to the Rad-signature and clinical variables by Cox regression analysis. It was presented as a nomogram and evaluated by receiver operating characteristic curves, calibration curves, and decision curve analysis. And further evaluate the application value of Rad-Clinical model in clinical stages and targeted sequential therapy of HCC. RESULTS: Tumor size, Barcelona Clinic Liver Cancer (BCLC) stage, and radiomics score (Rad-score) were found to be independent risk factors for PFS after DEB-TACE treatment for unresectable HCC, with the Rad-Clinical model being the greatest predictor of PFS in these patients (hazard ratio: 2.08; 95% confidence interval: 1.56-2.78; P < 0.001) along with high 6 months, 12 months, 18 months, and 24 months area under the curves of 0.857, 0.810, 0.843, and 0.838, respectively. In addition, compared to the radiomics and clinical nomograms, the Radiological-Clinical nomogram also significantly improved the classification accuracy for PFS outcomes, based on the net reclassification improvement (45.2%, 95% CI 0.260-0.632, p < 0.05) and integrated discrimination improvement (14.9%, 95% CI 0.064-0.281, p < 0.05). Based on this model, low-risk patients had higher PFS than high-risk patients in BCLC-B and C stages (P = 0.021). Targeted sequential therapy for patients with high and low-risk HCC in BCLC-B stage exhibited significant benefits (P = 0.018, P = 0.012), but patients with high-risk HCC in BCLC-C stage did not benefit much (P = 0.052). CONCLUSION: The Rad-Clinical model may be favorable for predicting PFS in patients with unresectable HCC treated with DEB-TACE and for identifying patients who may benefit from targeted sequential therapy.

An Interferometric Synthetic Aperture Radar Tropospheric Delay Correction Method Based on a Global Navigation Satellite System and a Backpropagation Neural Network: More Suitable for Areas with Obvious Terrain Changes.

Atmospheric delay correction remains a major challenge for interferometric synthetic aperture radar (InSAR) technology. In this paper, we first reviewed several commonly used methods for tropospheric delay correction in InSAR. Subsequently, considering the large volume and high temporal resolution of global navigation satellite system (GNSS) station measurement data, we proposed a method for spatial prediction of the InSAR tropospheric delay phase based on the backpropagation (BP) neural network and GNSS zenith total delay (ZTD). Using 42 Sentinel-1 interferograms over the Los Angeles area in 2021 as an example, we validated the accuracy of the BP + GNSS method in spatially predicting ZTD and compared the correction effects of BP + GNSS and five other methods on interferograms using the standard deviation (StaD) and structural similarity (SSIM). The results demonstrated that the BP + GNSS method reduced the root-mean-square error (RMSE) in spatial prediction by approximately 95.50% compared to the conventional interpolation method. After correction using the BP + GNSS method, StaD decreased in 92.86% of interferograms, with an average decrease of 52.03%, indicating significantly better correction effects than other methods. The SSIM of the BP + GNSS method was lower in mountainous and high-altitude areas with obvious terrain changes in the east and north, exhibiting excellent and stable correction performance in different seasons, particularly outperforming the GACOS method in autumn and winter. The BP + GNSS method can be employed to generate InSAR tropospheric delay maps with high temporal and spatial resolution, effectively addressing the challenge of removing InSAR tropospheric delay signals in areas with significant terrain variations.

Characterization of bone marrow heterogeneity in NK-AML (M4/M5) based on single-cell RNA sequencing.

Normal karyotype acute myeloid leukemia (NK-AML) is a heterogeneous hematological malignancy that contains a minor population of self-renewing leukemia stem cells (LSCs), which complicate efforts to achieve long-term survival. We performed single-cell RNA sequencing to profile 39,288 cells from 6 bone marrow (BM) aspirates including 5 NK-AML (M4/M5) patients and 1 healthy donor. The single-cell transcriptome atlas and gene expression characteristics of each cell population in NK-AML (M4/M5) and healthy BM were obtained. In addition, we identified a distinct LSC-like cluster with possible biomarkers in NK-AML (M4/M5) and verified 6 genes using qRT‒PCR and bioinformatic analyses. In conclusion, we utilized single-cell technologies to provide an atlas of NK-AML (M4/M5) cell heterogeneity, composition, and biomarkers with implications for precision medicine and targeted therapies.

An immune-related gene prognostic index for acute myeloid leukemia associated with regulatory T cells infiltration.

Acute myeloid leukemia (AML) is a malignant clonal disease characterized by abnormal proliferation of immature myeloid cells and bone marrow failure. Regulatory T cells (Treg) play a suppressive role in the anti-tumor immune response in the tumor microenvironment. Screening biomarkers based on Treg immune-related genes may help to predict the prognosis and the efficacy of immunotherapy of AML.Gene expression profiles of AML (non-M3) were obtained from the TCGA and GEO databases. Gene module related to Treg was extracted using CIBERSORT and WGCNA algorithms. Univariate Cox regression and LASSO analyses were performed to identify hub genes and constructed the immune prognostic model. Molecular and immunological features associated with risk signature were explored, and TIDE was used to predict the efficacy of immunotherapy.A risk signature was constructed based on the five IRGs (IFI27L1, YIPF6, PARVB, TRIM32 and RHOBTB3). The risk signature could be served as an independent prognostic factor of AML. Patients in the high-risk group had a poorer OS than those in the low-risk group. In addition, patients in the high-risk group had higher TP53 mutation rate, higher infiltration of Treg, higher immune escape potential and less benefit from ICI therapy compared to low-risk group.Our study constructed a prognostic index based on five Treg-related biomarkers, which help to facilitate the differentiation of immunological and molecular characteristics of AML, predict patient prognosis and provide a reference for predicting benefits from ICI therapy.

Expression and clinical significance of RAG1 in myelodysplastic syndromes.

OBJECTIVE: To determine the expression level of RAG1 and its clinical significance in myelodysplastic syndromes (MDS). METHODS: To explore the candidate genes, the microarray datasets GSE19429, GSE58831, and GSE2779 were downloaded from the Gene Expression Omnibus database. The differentially expressed genes (DEGs) in MDS were screened using RStudio, and overlapped DEGs were obtained with Venn Diagrams. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses, and protein-protein interaction network were performed. Quantitative real-time PCR (qRT-PCR) was employed to confirm the microarray results. RESULTS: This study identified 26 DEGs. Functional enrichment analyses indicated that these DEGs were significantly enriched in the immune response, and hematopoietic cell lineage. Eight core genes, for example, RAG1 and PAX5, were identified with a high degree of connectivity. The result of qRT-PCR showed that RAG1 was significantly down-regulated in MDS patients, which helped in distinguishing MDS patients from normal controls. The area under the curve of the receiver operator characteristic was 0.913 (P < 0.0001). MDS patients with low RAG1 expression level had a poor long-term survival (P = 0.031). What's more, the expression of RAG1 was significantly increased in the patients who received treatment. CONCLUSION: The results showed that the expression of RAG1 was down-regulated in MDS patients. Lower RAG1 expression was associated with adverse clinical outcomes. RAG1 may be a potential prognostic biomarker for MDS.

[Effects of Paclitaxel and Quizartinib Alone and in Combination on AML Cell Line MV4-11 and Its STAT5 Signal Pathway].

OBJECTIVE: To investigate the effects of paclitaxel, quizartinib and their combination on proliferation, apoptosis and FLT3/STAT5 pathway of human leukemia cell line MV4-11 (FLT3-ITD+). METHODS: MV4-11 cells were treated with paclitaxel and quizartinib at different concentrations for 24 h, 48 h and 72 h, respectively, and then the two drugs were combined at 48 h to compare the inhibition of proliferation, the apoptosis rate was detected by flow cytometry, the expression of FLT3 and STAT5 mRNA was determined by fluorescence quantitative PCR, and the protein expression of FLT3, p-FLT3, STAT5 and p-STAT5 was determined by Western blot. RESULTS: Different combination groups of paclitaxel and quizartinib had synergistic inhibitory effect. The cell survival rate in the combination group was significantly lower than that in the single drug group (P<0.05). The cell apoptosis rate in the combination group was significantly higher than that in the single drug group (P<0.001). The expression of FLT3 mRNA in combination group was significantly higher than that in two single drugs (P<0.01). The expression of STAT5 mRNA in combination group was significantly higher than that in quizartinib group (P<0.001); increased compared with paclitaxel group, but there was no statistical significance. The expression level of p-FLT3、p-STAT5 protein in the combination group was significantly lower than that in the single drug group (P<0.05, P<0.05). CONCLUSION: Paclitaxel combined with quizartinib can synergistically inhibit the proliferation of MV4-11 cell line and promote the apoptosis of MV4-11 cell line by inhibiting the activity of FLT3/STAT5 pathway.

5-Amino-4-Imidazolecarboxamide Ribonucleotide Transformylase/IMP Cyclohydrolase Polymorphisms Affect the Susceptibility to Multiple Myeloma.

OBJECTIVE: The upregulation of 5-amino-4-imidazolecarboxamide ribonucleotide transformylase/IMP cyclohydrolase (ATIC) may affect tumorigenesis and multiple myeloma (MM) development. MATERIALS AND METHODS: A total of 97 patients with MM and 102 healthy control patients were included in the study. The SNaPshot technique was used to detect the ATIC gene polymorphisms. Linkage disequilibrium (LD) and haplotype analyses were conducted using SHEsis software. RESULTS: The genotype distribution or allele frequency of rs3772078 and rs16853834 was significantly different between the patients with MM and the healthy control patients (all P < .05). The rs16853834 A allele, rs3772078 CT genotype, and C allele were associated with a decreased risk of MM (all P < .05). Five single-nucleotide polymorphism combinations showed strong LD. Three haplotypes were associated with MM risk (all P < .05). We found that ATIC rs7604984 was significantly associated with serum lactate dehydrogenase levels (P = .050). CONCLUSION: We determined that the rs3772078 and rs16853834 polymorphisms are associated with a decreased risk of MM.

Reconstructing the data gap between GRACE and GRACE follow-on at the basin scale using artificial neural network.

The Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO) observations, have been used to monitor the terrestrial water storage (TWS) change for almost 20 years. But the nearly 1-year gap between GRACE and GRACE-FO breaks the continuity of the observations, which influences the study on short-term TWS change and may introduce biases in GRACE (FO)-based data analysis. In this study, we propose to combine multichannel singular spectrum analysis (MSSA) and back propagation neural network (BPNN) to reconstruct this data gap. We use the MSSA first to initially interpolate the missing GRACE TWS data and second to decompose the hydroclimatic driving data and the target GRACE TWS data into partially reconstructed components (RC) and then use the BPNN to establish the relationships between each target RC and driving RCs. To reasonably test the model performance, we customize a sliding window test method that uses a 1-year window to determine the training and testing data so that we can approximate the real case. Using the proposed methods, we reconstruct the TWS data gaps in 28 hot areas that suffered severe TWS changes with a mean root mean square error (RMSE) of 2.7 cm and in 26 major river basins with a mean RMSE of 2.2 cm. This combined method outperforms the MSSA-based methods and most artificial neural network-based methods. Given the fact that the nominal accuracy of GRACE is ~2 cm and the TWS changes were large in the hot areas, the reconstruction accuracy is impressive. This study is expected to provide an advanced method for gap filling, data reconstruction, and data fusion as well as provide high-quality continuous TWS data for hydrological and climatic studies, especially in the 28 hot areas where no reconstructed data are available.

Clinical Significance of Decreased GPX1 Expression in Patients with Acute Myeloid Leukaemia (Non-M3).

OBJECTIVE: To determine the expression levels of GPX1, SOCS5 and IL7 and their clinical significance in patients with acute myeloid leukaemia (AML). STUDY DESIGN: A case-control study. PLACE AND DURATION OF STUDY: Department of Hematology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China, from January 2013 to November 2020. METHODOLOGY: Based on the bioinformatics analysis, the expression levels of GPX1, SOCS5 and IL7 in the bone marrow of 64 AML patients (non-M3) and 32 healthy individuals were evaluated by real-time PCR. Correlation between GPX1 expression and the clinical characteristics, response to induced chemotherapy, and survival time of AML patients were analysed as the outcome measure. RESULTS: GPX1 was significantly downregulated in AML patients, which helped in distinguishing AML patients from normal controls. The area under the curve (AUC) of the receiver operator characteristic (ROC) was 0.741 (p <0.001). Additionally, GPX1 expression was correlated with gender (r = -0.250, p = 0.045), FAB classification (r = -0.332,  p = 0.004), and chemotherapy response (r = 0.366, p = 0.003). AML patients with high GPX1 expression levels had a lower rate of remission (p = 0.021) and poor long-term survival (p = 0.036) than those with low GPX1 expression levels. CONCLUSION: Low GPX1 expression in AML patients may be closely associated with the pathogenesis and chemoresistance of AML. Key Words: Acute myeloid leukaemia, Clinical outcome, Gene expression, GPX1.

[Exploring the Mechanism of Paclitaxel Inhibiting T-cell Lymphoma based on High-throughput Sequencing and Public Databases].

OBJECTIVE: To analyze gene expression profile of T cell lymphoma Jurkat cell line treated with paclitaxel by computational biology based on next generation sequencing and to explore the possible molecular mechanism of paclitaxel resistance to T cell lymphoma at gene level. METHODS: IC50 of paclitaxel on Jurkat cell line was determined by CCK-8 assay. Gene expression profile of Jurkat cells treated with paclitaxel was acquired by next generation sequencing technology. Gene microarray data related to human T cell lymphoma were screened from Gene Expression Omnibus (GEO) database (including 720 cases of T cell lymphoma and 153 cases of normal tissues). Combined with the sequencing data, differential expression genes (DEGs) were intersected and screened. DAVID database was used for enrichment analysis of GO function and KEGG pathway to determine and visualize functional entries of DEGs, and protein-protein interactions network of DEGs was drawn. The levels of gene expression were detected and verified by RT-qPCR. RESULTS: CCK-8 results showed that the proliferation of Jurkat cells was inhibited by paclitaxel depended on the concentration apparently. Treated by paclitaxel for 48 h, P<0.05 and |log2(FC)|≥1 were used as filter criteria on the results of RNA Sequencing (RNA-Seq) and GeoChip, 351 DEGs were found from Jurkat cells, including 323 up-regulated genes and 28 down-regulated genes. The GO functional annotation and KEGG pathway enrichment analysis showed that the role of paclitaxel was mainly concentrated in protein heterodimerization activity, nucleosome assembly and transcriptional dysregulation in cancer, etc. The results of RT-qPCR were consistent with those of the sequencing analysis, which verified the reliability of this sequencing. CONCLUSION: Paclitaxel can affect the proliferation and apoptosis of T-cell lymphoma by up-regulating JUN gene, orphan nuclear receptor NR4A family genes and histone family genes.

Study of Spatial and Temporal Variations of Ionospheric Total Electron Content in Japan, during 2014-2019 and the 2016 Kumamoto Earthquake.

There are a large number of excellent research cases in Global Navigation Satellite System (GNSS) positioning and disaster prediction in Japan region, where the simulation and prediction of total electron content (TEC) is a powerful research method. In this study, we used the data of the GNSS Earth Observation Network (GEONET) established by the Geographical Survey Institute of Japan (GSI) to compare the performance of two regional ionospheric models in Japan, in which the spherical cap harmonic (SCH) model has the best performance. In this paper, we investigated the spatial and temporal variations of ionospheric TEC in Japan and their relationship with latitude, longitude, seasons, and solar activity. The results show that the TEC in Japan increases as the latitude decreases, with the highest average TEC in spring and summer and the lowest in winter, and has a strong correlation with solar activity. In addition, the observation and analysis of ionospheric disturbances over Japan before the 2016 Kumamoto earthquake and geomagnetic storms showed that GNSS observing of ionospheric TEC seems to be very effective in forecasting natural disasters and monitoring space weather.

Accuracy Analysis of International Reference Ionosphere 2016 and NeQuick2 in the Antarctic.

Global navigation satellite system (GNSS) can provide dual-frequency observation data, which can be used to effectively calculate total electron content (TEC). Numerical studies have utilized GNSS-derived TEC to evaluate the accuracy of ionospheric empirical models, such as the International Reference Ionosphere model (IRI) and the NeQuick model. However, most studies have evaluated vertical TEC rather than slant TEC (STEC), which resulted in the introduction of projection error. Furthermore, since there are few GNSS observation stations available in the Antarctic region and most are concentrated in the Antarctic continent edge, it is difficult to evaluate modeling accuracy within the entire Antarctic range. Considering these problems, in this study, GNSS STEC was calculated using dual-frequency observation data from stations that almost covered the Antarctic continent. By comparison with GNSS STEC, the accuracy of IRI-2016 and NeQuick2 at different latitudes and different solar radiation was evaluated during 2016-2017. The numerical results showed the following. (1) Both IRI-2016 and NeQuick2 underestimated the STEC. Since IRI-2016 utilizes new models to represent the F2-peak height (hmF2) directly, the IRI-2016 STEC is closer to GNSS STEC than NeQuick2. This conclusion was also confirmed by the Constellation Observing System for Meteorology Ionosphere and Climate (COSMIC) occultation data. (2) The differences in STEC of the two models are both normally distributed, and the NeQuick2 STEC is systematically biased as solar radiation increases. (3) The root mean square error (RMSE) of the IRI-2016 STEC is smaller than that of the NeQuick2 model, and the RMSE of the two modeling STEC increases with solar radiation intensity. Since IRI-2016 relies on new hmF2 models, it is more stable than NeQuick2.

Association of Growth Differentiation Factor-15 Polymorphisms and Growth Differentiation Factor-15 Serum Levels with Susceptibility to Multiple Myeloma in a Chinese Population.

BACKGROUND: The aim of our study was to evaluate the relationship between growth differentiation factor-15 (GDF15) rs1058587, rs4808793, and rs1059369 polymorphisms, serum concentrations of GDF15, and International Staging System (ISS) staging or Durie-Salmon staging system (DS) staging in multiple myeloma patients and whether its polymorphism affects the expression of serum GDF15 in Chinese population. METHODS: A total of 120 patients with multiple myeloma and 119 healthy controls were included in the study. The SNaPshot technique was used to detect the GDF15 gene polymorphisms. Serum GDF15 levels were measured using an Enzyme-Linked ImmunoSorbent Assay (ELISA) kit. RESULTS: There was no significant difference in genotype distribution or allele frequency of three loci between multiple myeloma patients and healthy controls. However, the genotype distribution and allele frequencies of rs1059369 in ISS stage I were significantly different from those in ISS stage II (p = 0.008), and the distribution of rs1058587 genotype was different between ISS stage II and ISS stage III (p = 0.014). The overall serum concentration of GDF15 and the same genotype at the same locus (rs1058587: GC, GG; rs4808793: CC, GC; rs1059369: AA, AT, and TT) in patients with multiple myeloma was significantly higher than in the healthy control group (all p < 0.05). CONCLUSIONS: Our results showed the genotype distribution and allele frequencies of rs1059369 and rs1058587 of GDF15 gene have some association with ISS and DS stage. But the polymorphism of GDF15 did not affect the expression of serum GDF15 in patients with multiple myeloma.

Identification the prognostic value of glutathione peroxidases expression levels in acute myeloid leukemia.

BACKGROUND: Glutathione peroxidases (GPXs) are an enzyme family with peroxidase activity. Abnormal GPX expression is associated with carcinogenesis. However, the potential role of the GPX gene family in acute myeloid leukemia (AML) remains to be comprehensively examined. METHODS: We analyzed GPX mRNA expression levels and determined the correlation between gene expression and the prognostic value via multiple universally acknowledged databases including the Oncomine, Gene Expression Profiling Interactive Analysis (GEPIA), PROGgeneV2, UALCAN, Cancer Cell Line Encyclopedia (CCLE), and The European Bioinformatics Institute (EMBL-EBI) databases. The functional network of differentially expressed GPXs was investigated via the NetworkAnalyst platform. Correlated genes as well as kinase, microRNA (miRNA), and transcription factor (TF) targets were identified using LinkedOmics. RESULTS: We observed that the transcriptional expression levels of GPX-1, -2, -4, -7, and -8 had significant difference between AML patients samples and normal samples, and that AML patients with high expression of GPX-1, -3, -4, and -7 were associated with poorer prognosis of overall survival (OS). Functional enrichment analysis showed that the differentially expressed GPXs were mainly enriched in response to oxidative stress, regulation of immune response, and inflammatory response, along with glutathione metabolism and ferroptosis. Overexpression of correlated genes, PSMB10, VPS13D, NDUFS8, ATP5D, POLR2E, and HADH were linked to adverse OS in AML. Regulatory network analysis indicated that differentially expressed GPXs regulated cell proliferation, cancer progression, apoptosis, and cell cycle signaling via pathways involving cancer-related kinases (such as DAPK1 and SRC), miRNAs (such as miR-202 and miR-181), and TFs (such as SRF and E2F1). CONCLUSIONS: Our findings offer novel insights into the differential expression and prognostic potential of the GPX family in AML, and lay a foundation for subsequent research of GPX's role in the carcinogenesis and regulatory network of AML.

Analysis of Ionospheric Disturbances Caused by the 2018 Bering Sea Meteor Explosion Based on GPS Observations.

The Bering Sea meteor explosion that occurred on 18 December 2018 provides a good opportunity to study the ionospheric disturbances caused by meteor explosions. Total electron content (TEC) is the core parameter of ionospheric analysis. TEC and its changes can be accurately estimated based on the Global Positioning System (GPS). TID is detected in time and frequency domain based on power spectrum and Butterworth filtering method. By analyzing the waveform, period, wavelength, propagation speed and space-time distribution of TID, the location of the TID source is determined, and the process of TID formation and propagation is understood. The TID caused by meteor explosions has significant anisotropy characteristic. Two types of TID were found. For the first type, the average horizontal propagation velocity is 250.22 ± 5.98 m/s, the wavelength is ~135-240 km, the average period is about 12 min, and the propagation distance is less than 1400 km. About 8 min after the meteor explosion, the first type of TID source formed and propagated radially at the velocity of 250.22 ± 5.98 m/s. For the second type, the propagation velocity is ~434.02 m/s. According to the waveform, period, wavelength and propagation velocity of the TID, it is diagnosed to be the midscale traveling ionospheric disturbances (MSTID). Based on the characteristics of TID, we infer that the TID is excited by the gravity waves generated by the meteor explosion, which is in accordance with the propagation law of gravity waves in the ionosphere. And it is estimated that the average velocity of the up-going gravity waves is about 464.58 m/s. A simple model was established to explain the formation and the propagation of this TID, and to verify the characteristics of the TID propagation caused by nuclear explosion, earthquake, tsunami, and Chelyabinsk meteorite blast. It is estimated that the position of the TID source is consistent with the meteor explosion point, which further indicates that the TID is caused by the meteor explosion and propagates radially.

Short-term rainfall forecast model based on the improved BP-NN algorithm.

The existing methods have been used the Zenith Total Delay (ZTD) or Precipitable Water Vapor (PWV) derived from Global Navigation Satellite System (GNSS) for rainfall forecasting. However, the occurrence of rainfall is highly related to a myriad of atmospheric parameters, and a good forecast result cannot be obtained if it only depends on a single predictor. This study focused on rainfall forecasting by using a number of atmospheric parameters (such as: temperature, relative humidity, dew temperature, pressure, and PWV) based on the improved Back Propagation Neural Network (BP-NN) algorithm. Results of correlation analysis showed that each meteorological parameter contributed to rainfall. Therefore, a short-term rainfall forecast model was proposed based on an improved BP-NN algorithm by using multiple meteorological parameters. Two GNSS stations and collocated weather stations in Singapore were used to validate the proposed rainfall forecast model by using three years of data (2010-2012). True forecast (TFR), false forecast (FFR), and missed forecast (MFR) rate were introduced as evaluation indices. The experimental result revealed that the proposed model exhibited good performance with TFR larger than 96% and FFR of approximately 40%. The proposed method improved TFR by approximately 10%, whereas FFR was comparable to existing literature. This forecasted result further verified the reliability and practicability of the proposed rainfall forecasting method by using the improved BP-NN algorithm.

[Effect of Mangiferin on Proliferation of FLT3-ITD Mutation Positive Acute Myeloid Leukemia Cell MV4-11 and Its Mechanism].

OBJECTIVE: To investigate the effects of mangiferin on proliferation, apoptosis and cycle of FLT3-ITD mutation-positive acute myeloid leukemia cells and its mechanism. METHODS: The effects of different concentration of mangiferin on proliferation of MV4-11 cells were detected by CCK8 method. Apoptosis, cell cycle and FLT3 transmembrane protein expression were detected by flow cytometry. FLT3 mRNA expression was detected by real-time fluorescent quantitative polymerase chain reaction (PCR) . RESULTS: Mangiferin obviously inhibited MV4-11 proliferation in a concentration and time dependent manner (48 h，r=0.922；72 h，r=0.959；96 h，r=0.973). The ratio of G0/G1 phase in cell cycle increased with the enhancement of concentration of mangiferin in MV4-11 cells for 48 h, and the ratio of S phase decreased with enhasment of concentration. The increase of apoptosis was more obvious. The expression of FLT3 transmembrane protein significantly decreased after the actior of IC50 concentration of mangiferin in MV4-11 cells for 48 h. The results of qRT-PCR showed that the expression of FLT3 mRNA significantly decreased after treatment of MN4-11 cells with mangiferin (P＜0.05). CONCLUSION: Mangiferin inhibits MV4-11 cell proliferation, arrests cell cycle progression and promotes apoptosis, which may be related with the inhibition of FLT3 activity by mangiferin and the subsequent signaling pathways involved in apoptosis and proliferation of cells.

A New GPS SNR-based Combination Approach for Land Surface Snow Depth Monitoring.

Snow is not only a critical storage component in the hydrologic cycle but also an important data for climate research; however, snowfall observations are only sparsely available. Signal-to-noise ratio (SNR) has recently been applied for sensing snow depths. Most studies only consider either global positioning system (GPS) L1 or L2 SNR data. In the current study, a new snow depth estimation approach is proposed using multipath reflectometry and SNR combination of GPS triple frequency (i.e. L1, L2 and L5) signals. The SNR combination method describes the relationship between antenna height variation and spectral peak frequency. Snow depths are retrieved from the SNR combination data at YEL2 and KIRU sites and validated by comparing it with in situ observations. The elevation angle ranges from 5° to 25°. The correlations for the two sites are 0.99 and 0.97. The performance of the new approach is assessed by comparing it with existing models. The proposed approach presents a high correlation of 0.95 and an accuracy (in terms of Root Mean Square Error) improvement of over 30%. Findings indicate that the new approach could potentially be applied to monitor snow depths and may serve as a reference for building multi-system and multi-frequency global navigation satellite system reflectometry models.