Pressure Induced Suppression to the Valence Change Transition in EuPdAs

By applying a hydrostatic pressure, we have successfully suppressed the valence change transition in EuPdAs. The studied compound EuPdAs crystallizes in a P63/mmc space group. Through resistivity and magnetic susceptibility measurements, we find that EuPdAs shows a phase transition at 180 K and another transition below 10 K at ambient pressure, as was reported before. The overall transport and magnetic behavior is to some extent similar to that of the parent phase of iron based superconductors. With application of a hydrostatic pressure, the transition at 180 K is sensitively suppressed with a pressure as low as 0.48 GPa. However, superconductivity has not been induced with pressure up to 1.90 GPa

Ferroptosis and its potential role in gestational diabetes mellitus: updated evidence from pathogenesis to therapy

BackgroundStudies have demonstrated that high iron status is positively associated with gestational diabetes mellitus (GDM), implying that iron overload and ferroptosis play important roles in the development of GDM. The aim of this study was to explore effective therapeutic drugs from traditional Chinese medicine (TCM)formulas for the treatment of GDM based on ferroptosis.MethodsIn this study, the presence of ferroptosis in the placenta was verified through clinical and experimental data, and key genes were subsequently screened for association with ferroptosis in the development of GDM. The analysis was based on transcriptome sequencing of datasets combined with differentially expressed genes (DEGs) analysis and weighted gene correlation network analysis (WGCNA); functional enrichment analysis was also performed. A protein−protein interaction (PPI) network was constructed and pivotal genes were identified using Cytoscape. Finally, traditional Chinese medicine (TCM)formulas related to treating GDM were collected, then the proteins corresponding to the key genes were molecularly docked with the small molecular structures of clinically proven effective herbal tonics, and molecular dynamic simulations were performed to select the best candidates for pharmacological compounds.ResultsElevated ferritin levels in patients with GDM were verified using clinical data. The presence of ferroptosis in placental tissues of patients with GDM was confirmed using electron microscopy and western blotting. Ninety-nine key genes with the highest correlation with ferroptosis were identified from DEGs and weighted gene co-expression network analysis (WGCNA). Analysis using the Kyoto Encyclopedia of Genes and Genomes demonstrated that the DEGs were primarily involved in the oxidative phosphorylation pathway. The key genes were further screened by PPI; two key genes, SF3B14 and BABAM1, were identified by combining the gene corresponding to protein structure and function, followed by molecular docking and molecular dynamic simulation. Coptis chinensis was proposed as the best candidate for herbal treatment at the molecular level.ConclusionThis data revealed the presence of ferroptosis in patients with GDM and identified possible modulatory roles of ferroptosis-related genes involved in the molecular mechanisms of GDM, providing new insights into the pathogenesis of GDM, which also provided new directions for the systematic optimization of TCM formulas for the management and targeted treatment of GDM

Automatic Scaling Hadoop in the Cloud for Efficient Process of Big Geospatial Data

Efficient processing of big geospatial data is crucial for tackling global and regional challenges such as climate change and natural disasters, but it is challenging not only due to the massive data volume but also due to the intrinsic complexity and high dimensions of the geospatial datasets. While traditional computing infrastructure does not scale well with the rapidly increasing data volume, Hadoop has attracted increasing attention in geoscience communities for handling big geospatial data. Recently, many studies were carried out to investigate adopting Hadoop for processing big geospatial data, but how to adjust the computing resources to efficiently handle the dynamic geoprocessing workload was barely explored. To bridge this gap, we propose a novel framework to automatically scale the Hadoop cluster in the cloud environment to allocate the right amount of computing resources based on the dynamic geoprocessing workload. The framework and auto-scaling algorithms are introduced, and a prototype system was developed to demonstrate the feasibility and efficiency of the proposed scaling mechanism using Digital Elevation Model (DEM) interpolation as an example. Experimental results show that this auto-scaling framework could (1) significantly reduce the computing resource utilization (by 80% in our example) while delivering similar performance as a full-powered cluster; and (2) effectively handle the spike processing workload by automatically increasing the computing resources to ensure the processing is finished within an acceptable time. Such an auto-scaling approach provides a valuable reference to optimize the performance of geospatial applications to address data- and computational-intensity challenges in GIScience in a more cost-efficient manner

Intermittent Hypoxia Exposure Helps to Restore the Reduced Hemoglobin Concentration During Intense Exercise Training in Trained Swimmers

In prolonged intense exercise training, the training load of athletes may be reduced once their hemoglobin concentrations ([Hb]s) are decreased dramatically. We previously reported that intermittent hypoxia exposure (IHE) could be used to alleviate the decrease of [Hb] and help to maintain the training load in rats. To further explore the feasibility of applying IHE intervention to athletes during prolonged intense exercise training, 6 trained swimmers were recruited to conduct a 4-week IHE intervention at the intervals after their [Hb] dropped for 10% or more during their training season. IHE intervention lasted 1 h and took place once a day and five times a week. Hematological and hormonal parameters, including [Hb], red blood cells (RBC), hematocrit (Hct), reticulocytes, serum erythropoietin (EPO), testosterone (T) and cortisol (C) were examined. After the IHE intervention was launched, [Hb], RBC and Hct of the subjects were increased progressively with their maximum levels (P < 0.01) showing at the third or fourth week, respectively. An increase in reticulocyte count (P < 0.01) suggests that IHE intervention promotes erythropoiesis to increase [Hb]. Besides, serum level of EPO, the hormone known to stimulate erythropoiesis, was overall higher than that before the IHE intervention, although it was statistically insignificant. Furthermore, the serum level of T, another hormone known to stimulate erythropoiesis, was increased progressively with the maximum level showing at the fourth week. Collectively, this study further confirms that IHE intervention may be used as a new strategy to prevent intense exercise training-induced reductions in [Hb]

Identification of candidate genes and clarification of the maintenance of the green pericarp of weedy rice grains

The weedy rice (Oryza sativa f. spontanea) pericarp has diverse colors (e.g., purple, red, light-red, and white). However, research on pericarp colors has focused on red and purple, but not green. Unlike many other common weedy rice resources, LM8 has a green pericarp at maturity. In this study, the coloration of the LM8 pericarp was evaluated at the cellular and genetic levels. First, an examination of their ultrastructure indicated that LM8 chloroplasts were normal regarding plastid development and they contained many plastoglobules from the early immature stage to maturity. Analyses of transcriptome profiles and differentially expressed genes revealed that most chlorophyll (Chl) degradation-related genes in LM8 were expressed at lower levels than Chl a/b cycle-related genes in mature pericarps, suggesting that the green LM8 pericarp was associated with inhibited Chl degradation in intact chloroplasts. Second, the F2 generation derived from a cross between LM8 (green pericarp) and SLG (white pericarp) had a pericarp color segregation ratio of 9:3:4 (green:brown:white). The bulked segregant analysis of the F2 populations resulted in the identification of 12 known genes in the chromosome 3 and 4 hotspot regions as candidate genes related to Chl metabolism in the rice pericarp. The RNA-seq and sqRT-PCR assays indicated that the expression of the Chl a/b cycle-related structural gene DVR (encoding divinyl reductase) was sharply up-regulated. Moreover, genes encoding magnesium-chelatase subunit D and the light-harvesting Chl a/b-binding protein were transcriptionally active in the fully ripened dry pericarp. Regarding the ethylene signal transduction pathway, the CTR (encoding an ethylene-responsive protein kinase) and ERF (encoding an ethylene-responsive factor) genes expression profiles were determined. The findings of this study highlight the regulatory roles of Chl biosynthesis- and degradation-related genes influencing Chl accumulation during the maturation of the LM8 pericarp

A SqueeSAR Spatially Adaptive Filtering Algorithm Based on Hadoop Distributed Cluster Environment

Multi-temporal interferometric synthetic aperture radar (MT-InSAR) techniques analyze a study area using a set of SAR image data composed of time series, reaching millimeter surface subsidence accuracy. To effectively acquire the subsidence information in low-coherence areas without obvious features in non-urban areas, an MT-InSAR technique, called SqueeSAR, is proposed to improve the density of the subsidence points in the study area by fusing the distributed scatterers (DS). However, SqueeSAR filters the DS points individually during spatial adaptive filtering, which requires significant computer memory, which leads to low processing efficiency, and faces great challenges in large-area InSAR processing. We propose a spatially adaptive filtering parallelization strategy based on the Spark distributed computing engine in a Hadoop distributed cluster environment, which splits the different DS pixel point data into different computing nodes for parallel processing and effectively improves the filtering algorithm&rsquo;s performance. To evaluate the effectiveness and accuracy of the proposed method, we conducted a performance evaluation and accuracy verification in and around the main city of Kunming with the original Sentinel-1A SLC data provided by ESA. Additionally, parallel calculation was performed in a YARN cluster comprising three computing nodes, which improved the performance of the filtering algorithm by a factor of 2.15, without affecting the filtering accuracy

A SqueeSAR Spatially Adaptive Filtering Algorithm Based on Hadoop Distributed Cluster Environment

Multi-temporal interferometric synthetic aperture radar (MT-InSAR) techniques analyze a study area using a set of SAR image data composed of time series, reaching millimeter surface subsidence accuracy. To effectively acquire the subsidence information in low-coherence areas without obvious features in non-urban areas, an MT-InSAR technique, called SqueeSAR, is proposed to improve the density of the subsidence points in the study area by fusing the distributed scatterers (DS). However, SqueeSAR filters the DS points individually during spatial adaptive filtering, which requires significant computer memory, which leads to low processing efficiency, and faces great challenges in large-area InSAR processing. We propose a spatially adaptive filtering parallelization strategy based on the Spark distributed computing engine in a Hadoop distributed cluster environment, which splits the different DS pixel point data into different computing nodes for parallel processing and effectively improves the filtering algorithm’s performance. To evaluate the effectiveness and accuracy of the proposed method, we conducted a performance evaluation and accuracy verification in and around the main city of Kunming with the original Sentinel-1A SLC data provided by ESA. Additionally, parallel calculation was performed in a YARN cluster comprising three computing nodes, which improved the performance of the filtering algorithm by a factor of 2.15, without affecting the filtering accuracy

A Lightweight YOLOv5-MNE Algorithm for SAR Ship Detection

Unlike optical satellites, synthetic aperture radar (SAR) satellites can operate all day and in all weather conditions, so they have a broad range of applications in the field of ocean monitoring. The ship targets’ contour information from SAR images is often unclear, and the background is complicated due to the influence of sea clutter and proximity to land, leading to the accuracy problem of ship monitoring. Compared with traditional methods, deep learning has powerful data processing ability and feature extraction ability, but its complex model and calculations lead to a certain degree of difficulty. To solve this problem, we propose a lightweight YOLOV5-MNE, which significantly improves the training speed and reduces the running memory and number of model parameters and maintains a certain accuracy on a lager dataset. By redesigning the MNEBlock module and using CBR standard convolution to reduce computation, we integrated the CA (coordinate attention) mechanism to ensure better detection performance. We achieved 94.7% precision, a 2.2 M model size, and a 0.91 M parameter quantity on the SSDD dataset

Enabling big geoscience data analytics with a cloud-based, MapReduce-enabled and service-oriented workflow framework.

Geoscience observations and model simulations are generating vast amounts of multi-dimensional data. Effectively analyzing these data are essential for geoscience studies. However, the tasks are challenging for geoscientists because processing the massive amount of data is both computing and data intensive in that data analytics requires complex procedures and multiple tools. To tackle these challenges, a scientific workflow framework is proposed for big geoscience data analytics. In this framework techniques are proposed by leveraging cloud computing, MapReduce, and Service Oriented Architecture (SOA). Specifically, HBase is adopted for storing and managing big geoscience data across distributed computers. MapReduce-based algorithm framework is developed to support parallel processing of geoscience data. And service-oriented workflow architecture is built for supporting on-demand complex data analytics in the cloud environment. A proof-of-concept prototype tests the performance of the framework. Results show that this innovative framework significantly improves the efficiency of big geoscience data analytics by reducing the data processing time as well as simplifying data analytical procedures for geoscientists