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Research vessels are typically equipped with multiple receivers for positioning and attitude measurement; however, there is no data interaction or model fusion to implement the Precise Point Positioning (PPP) for these receivers. Therefore, a Constrained PPP (CPPP) is established by using the baseline length and tropospheric constraints of two receivers to improve the vessel’s remote marine positioning performance. Based on two integrated Real-Time Kinematic (RTK) receivers, marine experiments are conducted at a distance of 300–600 km from land. Compared to the ground environment, the Root Mean Square error (RMS) of the multipath and the STandard Deviation (STD) of the carrier-to-noise ratio in the marine environment are increased by 2.87 and 2.6 times, respectively. The length constraints reduce the RMS of positioning in the East, North, and Up (ENU) directions by about 0.053 (67%), 0.020 (34%), and 0.054 m (34%), respectively, and also rapidly recover positioning after interruptions, achieving positioning with errors in the ENU-directions of 10, 15, and 20 cm, respectively, within 3 minutes. When the baseline length and tropospheric constraints are combined, with or without the addition of the Zenith Wet Delay bias (dZWD), the average STD can be reduced by about 0.060 m (43%) and the accuracy of the up-positioning can be significantly improved. The correlation of dZWD to the up-positioning deviation of du=−2.161*dZWD is obtained by simulation. Since the acquisition of dZWD depends on the PPP accuracy, it is recommended that dZWD be set to compensate for unmodeled receiver errors when the STD of the up-positioning is superior to 0.06 m.

In this paper, the highland barley moisture, β-glucan, amylopectin and protein content rapid detection model was established based on the 4000~10000 cm−1 near-infrared spectrum and actual contents of 76 highland barleys. The results showed that SG convolution smoothing was the optimal spectral preprocessing method for the partial least squares (PLS) prediction model of moisture, amylopectin and β-glucan contents, while SG convolution smoothing+multiplicative scatter correction (MSC) was the optimal spectral preprocessing method for the PLS prediction model of protein content. In order to further improve the accuracy of the prediction model, the different characteristic wavelength selection algorithms including competitive adaptive reweighted sampling (CARS), successive projections algorithm (SPA) and variables combination population analysis and iterative retained information variable (VCPA-IRIV) were used on the prediction results of the model. The results showed that VCPA-IRIV treatment could effectively improve the determination coefficient of prediction of moisture, amylose and protein content prediction model, and reduce the root mean square error of prediction. The treatment of CARS had a remarkable effect on the prediction accuracy for the β-glucan content. Ultimately, the established prediction models of moisture, β-glucan, amylopectin and protein content for highland barley had good prediction accuracy with the appropriate Rp (0.9868, 0.9808, 0.9701 and 0.9879) and RMSEP (0.2042, 0.1846, 0.8135 and 0.2095) value, respectively. In conclusion, the rapid detection model of highland barley characteristic nutrient content based on near infrared spectroscopy established in this study had high accuracy, which would have certain guiding significance for processing enterprises to quickly understand the quality of raw materials and efficiently screen qualified raw materials.

Early-onset epilepsy following ischemic stroke is a severe neurological condition, the pathogenesis of which remains incompletely understood. Recent studies suggest that Neural stem/progenitor cells (NSPCs) play a crucial role in the disease process, yet the precise molecular mechanisms regulating NSPCs have not been thoroughly investigated. This study utilized single-cell transcriptome sequencing and bioinformatics analysis to identify disease-related genes, which were subsequently validated in both in vitro and in vivo experiments. The findings revealed that Hsp90aa1 (heat shock protein 90 kDa alpha, class A member 1), Jun proto-oncogene (JUN), and CC Motif Ligation 2 (Ccl2) constitute an important regulatory axis influencing the migration and differentiation of NSPCs, potentially impacting the onset and progression of early-onset epilepsy post-ischemic stroke. Additionally, the expression of Hsp90aa1 was found to influence the likelihood of seizure occurrence and the severity of brain ischemia.

In the field of multitemporal interferometric synthetic aperture radar (MT-InSAR), the selection of persistent scatterer (PS) is crucial for acquiring ground deformation product. To obtain precise ground deformation, pixels with as high signal-to-noise ratio (SNR) as possible should be selected, while pixels with low SNR should be avoided. To this end, we propose a novel framework, referred to as the PS feature-fusion network (PSFNet), for efficient PS selection. Specifically, we propose a data-driven two-branch network consisting of a ResUNet with spatial and channel attention, as well as a TANet with 3-D convolutional layers and a time-step attention block (T-Attention block), which can use not only spatial features of SAR image but also time-series phase features when selecting PS pixels. In particular, a time-step attention mechanism is proposed for accommodating to interferometric pairs with different SNRs to enhance the feature representation ability of the network. The proposed method was tested using the Sentinel-1 images, showing that it can select more PSs with higher quality compared with StaMPS. In addition, the prediction time of PSFNet requires only 0.26% of the running time of StaMPS, which greatly improves the efficiency of PSFNet for practical applications.

Abstract Background Research has indicated that long-term sleep deprivation can lead to immune dysfunction and participate in the occurance and progression of tumors. However, the relationship between sleep deprivation and colon cancer remains unclear. This study explored the specific mechanism through which sleep deprivation promotes the proliferation and migration of colon cancer, with a focus on the neurotransmitter GABA. Methods Chronic sleep deprivation mice model were used to investigate the effect of sleep disorder on tumors. We detected neurotransmitter levels in the peripheral blood of mice using ELISA. CCK-8 assay, colony formation assay, wound healing assay, and transwell assay were performed to investigate the effect of GABA on colon cancer cells, while immunofluorescence showed the distribution of macrophages in lung metastatic tissues. We isolated exosomes from a GABA-induced culture medium to explore the effects of GABA-induced colon cancer cells on macrophages. Gain- and loss-of-function experiments, luciferase report analysis, immunohistochemistry, and cytokine detection were performed to reveal the crosstalk between colon cancer cells and macrophages. Results Sleep deprivation promote peripheral blood GABA level and colon cancer cell proliferation and migration. Immunofluorescence analysis revealed that GABA-induced colon cancer metastasis is associated with enhanced recruitment of macrophages in the lungs. The co-culture results showed that GABA intensified M2 polarization of macrophage induced by colon cancer cells. This effect is due to the activation of the macrophage MAPK pathway by tumor-derived exosomal miR-223-3p. Furthermore, M2-like macrophages promote tumor proliferation and migration by secreting IL-17. We also identified an endogenous miR-223-3p downregulation of the E3 ligase CBLB, which enhances the stability of cMYC protein and augments colon cancer cells proliferation and migration ability. Notably, cMYC acts as a transcription factor and can also regulate the expression of miR-223-3p. Conclusion Our results suggest that sleep deprivation can promote the expression of miR-223-3p in colon cancer cells through GABA, leading to downregulation of the E3 ligase CBLB and inhibition of cMYC ubiquitination. Simultaneously, extracellular miR-223-3p promotes M2-like macrophage polarization, which leads to the secretion of IL-17, further enhancing the proliferation and migration of colon cancer cells.

Owing to continuous global warming, frozen soil degradation has become a universal phenomenon, leading to large-scale ground deformation that affects engineering construction and the fragile ecological balance. Geodetic observations, especially temporal InSAR, can quantify ground deformation. However, the accuracy of InSAR modelling in capturing the spatial–temporal variability of the freeze–thaw process depends on the spatial resolution of temperature measurements. This paper proposes a freeze–thaw amplitude model incorporating MODIS LST based on a single-master InSAR time-series deformation to calculate frozen soil deformation. We applied this model to the Qumalai-Zhiduo area of the Qinghai-Tibet Plateau and compared its results with those of the model using weather station temperature in terms of frozen soil deformation parameters, RSME, and characteristic targets. Our study found that the model incorporating MODIS LST performed better in areas far from weather stations, while both models produced similar results in areas of close proximity. Finally, we evaluated another commonly used method for calculating frozen soil deformation parameters and found that the method incorporating MODIS LST based on a single-master time-series deformation is more accurate and precise than the method based on a multi-master SBAS network.

Background PEBP (phosphatidyl ethanolamine-binding protein) is widely found in eukaryotes including plants, animals and microorganisms. In plants, the PEBP family plays vital roles in regulating flowering time and morphogenesis and is highly associated to agronomic traits and yields of crops, which has been identified and characterized in many plant species but not well studied in Tartary buckwheat (Fagopyrum tataricum Gaertn.), an important coarse food grain with medicinal value. Methods Genome-wide analysis of FtPEBP gene family members in Tartary buckwheat was performed using bioinformatic tools. Subcellular localization analysis was performed by confocal microscopy. The expression levels of these genes in leaf and inflorescence samples were analyzed using qRT-PCR. Results Fourteen Fagopyrum tataricum PEBP (FtPEBP) genes were identified and divided into three sub-clades according to their phylogenetic relationships. Subcellular localization analysis of the FtPEBP proteins in tobacco leaves indicated that FT- and TFL-GFP fusion proteins were localized in both the nucleus and cytoplasm. Gene structure analysis showed that most FtPEBP genes contain four exons and three introns. FtPEBP genes are unevenly distributed in Tartary buckwheat chromosomes. Three tandem repeats were found among FtFT5/FtFT6, FtMFT1/FtMFT2 and FtTFL4/FtTFL5. Five orthologous gene pairs were detected between F. tataricum and F. esculentum. Seven light-responsive, nine hormone-related and four stress-responsive elements were detected in FtPEBPs promoters. We used real-time PCR to investigate the expression levels of FtPEBPs among two flowering-type cultivars at floral transition time. We found FtFT1/FtFT3 were highly expressed in leaf and young inflorescence of early-flowering type, whereas they were expressed at very low levels in late-flowering type cultivars. Thus, we deduced that FtFT1/FtFT3 may be positive regulators for flowering and yield of Tartary buckwheat. These results lay an important foundation for further studies on the functions of FtPEBP genes which may be utilized for yield improvement.

The accumulation of liquid in gas wells is a common problem in the later stages of shale gas production. The plunger lift is currently one of the most economically effective methods for removing accumulated liquid in gas wells, but its applicability is not yet clear. This article analyzes the production characteristics of gas wells on-site and summarizes the key indicators for evaluating the plunger lift technology, including casing pressure changes, production changes, liquid column plunger height, tubing-casing pressure differential changes, and differential size of pressure relief. Combined with numerical simulation methods, a complete set of evaluation systems for judging, classifying, and evaluating the effectiveness of plunger lift technology is established, and an evaluation method for the applicability of plunger lift technology is proposed. Taking well Ning 216-H4 as an example, a comprehensive evaluation is made on whether the gas well is suitable for plunger lift based on the height of the liquid column above the plunger, the production effect under pressure, and the overall shut-in time. This method has important guiding significance for the further promotion and application of plunger lift technology in deep wells, directional wells, and horizontal wells to achieve efficient development of natural gas.

To meet the growing demand for socioeconomic development, a large amount of groundwater is extracted from confined aquifers worldwide. The North China Plain has experienced considerable groundwater depletion and subsidence during the past six decades. In this study, we use Sentinel-1A/B SAR images from 2015 to 2020 to map the ground subsidence of the Tianjin–Langfang area. Three subsiding zones centered at Guangyang, Wuqing–Bazhou, and Jinghai are identified with maximum subsidence rates of 98.1, 121.8, and 104.7 mm/yr. Seasonal and long-term signals are separated from time series subsidence and hydraulic measurements using continuous wavelet transform to retrieve aquifer parameters. The long-term subsidence, which fits well with an exponential decaying model, remarkably slows down in our study area. The elastic skeletal storage coefficients range between 0.52×10−3 and 9.66×10−3. We then retrieve the spatial–temporal variations of total groundwater storage, recoverable groundwater storage, and irreversible groundwater storage. Groundwater storage depletion rates are apparently reducing, which benefits from the operation of the South-to-North Water Transfer Project and local groundwater management practices.

The accurate estimation of forest aboveground biomass is of great significance for forest management and carbon balance monitoring. Remote sensing instruments have been widely applied in forest parameters inversion with wide coverage and high spatiotemporal resolution. In this paper, the capability of different remote-sensed imagery was investigated, including multispectral images (GaoFen-6, Sentinel-2 and Landsat-8) and various SAR (Synthetic Aperture Radar) data (GaoFen-3, Sentinel-1, ALOS-2), in aboveground forest biomass estimation. In particular, based on the forest inventory data of Hangzhou in China, the Random Forest (RF), Convolutional Neural Network (CNN) and Convolutional Neural Networks Long Short-Term Memory Networks (CNN-LSTM) algorithms were deployed to construct the forest biomass estimation models, respectively. The estimate accuracies were evaluated under the different configurations of images and methods. The results show that for the SAR data, ALOS-2 has a higher biomass estimation accuracy than the GaoFen-3 and Sentinel-1. Moreover, the GaoFen-6 data is slightly worse than Sentinel-2 and Landsat-8 optical data in biomass estimation. In contrast with the single source, integrating multisource data can effectively enhance accuracy, with improvements ranging from 5% to 10%. The CNN-LSTM generally performs better than CNN and RF, regardless of the data used. The combination of CNN-LSTM and multisource data provided the best results in this case and can achieve the maximum R2 value of up to 0.74. It was found that the majority of the biomass values in the study area in 2018 ranged from 60 to 90 Mg/ha, with an average value of 64.20 Mg/ha.

With the rapid development of modern Interferometric Synthetic Aperture Radar (InSAR) missions, SAR instruments with wider coverage can be used to monitor the ground surface deformation from regional to global scale. However, the ocean tide loading (OTL) displacement is becoming a primary source of errors. It contributes to a long-wavelength signal in InSAR interferograms, leading to errors from millimeter to centimeter-level in InSAR deformation monitoring, especially over coastal areas. Although the state-of-the-art has applied ocean tide models to mitigate the errors, the difference between them and their impact on InSAR measurements are rarely discussed. In this paper, we compare representative ocean tide models and investigate their effects in the correction of OTL errors. We found that (i) the modeled OTL displacements from different models show little difference over interiors far from the ocean, while disagreement becomes larger over coastal areas; (ii) the magnitude of OTL artifacts may be greater than the atmospheric delays in some coastal areas, and the correction using ocean tide models can effectively attenuate the OTL effects for large-scale InSAR measurements; (iii) when correcting the OTL errors for InSAR measurements, the global model TPXO and FES are recommended because of their better overall performance, while the NAO model performs the worst. The local models with high spatial resolution can help improve the capability of coarse global models in complex topographic areas.

The analysis of kinematic characteristics and triggering factors of the potentially unstable slopes is of great significance for the slope protection and landslide reinforcing. Located in Mao Country, Sichuan Province in China, Wadi village is a typical mountainous area where geological disasters frequently occur due to the complex geological environment, intense tectonic activities, and concentrated seasonal rainfall. However, the slope stability and potential risk over this area are not fully evaluated yet. In this article, we investigate the unstable slope in Wadi village using time-series synthetic aperture radar interferometry (InSAR) technique. The deformation history retrieved from 132 C-band descending Sentinel-1 images between October 2014 and September 2019 demonstrates that the Wadi landslide presents a slow sliding trend with the averaged line-of-sight velocity of −70 mm/year and has a distinct seasonal velocity at the head and toe area. We further decompose the predominant and periodic velocities by a kinematic model to analyses spatial and temporal characteristics of Wadi landslide. The results reveal that the Wadi landslide is a hybrid type of push and pull with two source areas in the head and toe of the slope, respectively. The periodic velocity variations are highly correlated with seasonal rainfall. Our study also demonstrates the importance of correcting InSAR unwrapping error for interpreting the landslide kinematics and other small-scale geomorphological processes.

In order to solve the difficulties and challenges in the implementation of the original blood distribution and collection regulations caused by the expansion of hospital area, the extension of blood transfer time, the changeability of blood transfer environment, and the strain of personnel due to the increase of workload, as well as to ensure the accuracy of the information throughout blood remote verification and distribution and the safety of clinical blood transfusion, , Shanghai experts related to clinical transfusion and blood management had made a systematic study on the applicable scope and management rules of remote verification of blood distribution and collection, and formulated this Expert Consensus combined with the development status of digital, intelligent and remote communication technologies, so as to provide corresponding guidance for clinical medical institutions in line with the changes in reality.

Landslides are frequent mountainous geohazards induced by multiple factors, such as extreme rainfall, river erosion, and intense anthropogenic activities. On 17 June 2020, a rainstorm hit Danba County, Sichuan Province, and triggered the catastrophic Aniangzhai landslide. The impact of this rainstorm on landslide kinematics in this region has seldom been investigated. We used a time-series interferometric synthetic aperture radar (InSAR) analysis to map the active slopes in Danba County using one ascending (2015–2021) and one descending (2018–2021) Sentinel-1 dataset. A total of 36 landslides (63.1 km2) were detected along riverbanks. The time series of eastward and vertical displacements during 2018–2021 were retrieved by integrating the ascending and descending Sentinel-1 datasets. The maximum eastward and vertical displacement rates obtained are 229 and −75 mm/yr, respectively. Seasonal accelerations are correlated with concentrated rainfall. We suggest that the 2020 rainstorm and the abundant precipitation during the water year 2020 altered the long-term displacement trends of active landslides. We further constrained the volumes of the Niela and Gaoding landslides in the order 3.6–6.9 × 107 m3 and 5.4–6.0 × 108 m3, respectively, using 2D displacement rates and mass conservation equations. Our results demonstrate that multi-temporal and multi-orbit InSAR measurements can provide insights into the evolution and mechanism of landslides.

Earth observation technologies have great potential in the investigation, monitoring and assessment of various geohazards. Stacking is an efficient InSAR method for estimating deformation rates and helps in the generation and update of the geohazard inventories. However, it relies on the assumption that the atmospheric statistics are stationary, which does not always hold in large-scale interferograms processing. The nonstationary signal, caused by turbulence and stratification of atmosphere, will bias the deformation estimate and lead to misinterpretations of the geophysical processes. In this paper, we propose an enhanced InSAR stacking method integrated with atmospheric correction. Atmospheric errors in the interferograms are first corrected, and then the mean deformation rate is estimated based on least squares. Applications are conducted in ground subsidence monitoring in the Yellow River Delta as well as landslide detection along the Jinsha River, China, with the deformation results evaluated by spatial structure function, semi-variogram and correlation. Spatial dependence in the subsidence results of the Yellow River Delta decreases from 757 km to 220 km, suggesting that the influence of atmospheric turbulence on deformation is mitigated. Correlation between deformation rate and elevation along the Jinsha River reduces from 0.40 to 0.15, indicating that stratification is suppressed. The proposed method adopts the strategies of simplicity and effectiveness, and the outcomes, which can meet the requirements of geohazards general survey, will be beneficial to rapid geohazard detection.

PurposePreviously, mutations in the voltage-gated calcium channel subunit alpha1 A (CACNA1A) gene have been reported to be associated with paroxysmal disorders, typically as episodic ataxia type 2. To determine the relationship between CACNA1A and epilepsies and the role of molecular sub-regional on the phenotypic heterogeneity.MethodsTrio-based whole-exome sequencing was performed in 318 cases with partial epilepsy and 150 cases with generalized epilepsy. We then reviewed all previously reported CACNA1A mutations and analyzed the genotype-phenotype correlations with molecular sub-regional implications.ResultsWe identified 12 CACNA1A mutations in ten unrelated cases of epilepsy, including four de novo null mutations (c.2963_2964insG/p.Gly989Argfs*78, c.3089 + 1G > A, c.4755 + 1G > T, and c.6340-1G > A), four de novo missense mutations (c.203G > T/p.Arg68Leu, c.3965G > A/p.Gly1322Glu, c.5032C > T/p.Arg1678Cys, and c.5393C > T/p.Ser1798Leu), and two pairs of compound heterozygous missense mutations (c.4891A > G/p.Ile1631Val& c.5978C > T/p.Pro1993Leu and c.3233C > T/p.Ser1078Leu&c.6061G > A/p.Glu2021Lys). The eight de novo mutations were evaluated as pathogenic or likely pathogenic mutations according to the criteria of American College of Medical Genetics and Genomics (ACMG). The frequencies of the compound heterozygous CACNA1A mutations identified in this cohort were significantly higher than that in the controls of East Asian and all populations (P = 7.30 × 10–4, P = 2.53 × 10–4). All of the ten cases were ultimately seizure-free after antiepileptic treatment, although frequent epileptic seizures were observed in four cases. Further analysis revealed that episodic ataxia type 2 (EA2) had a tendency of higher frequency of null mutations than epilepsies. The missense mutations in severe epileptic phenotypes were more frequently located in the pore region than those in milder epileptic phenotypes (P = 1.67 × 10–4); de novo mutations in the epilepsy with intellectual disability (ID) had a higher percentage than those in the epilepsy without ID (P = 1.92 × 10–3).ConclusionThis study suggested that CACNA1A mutations were potentially associated with pure epilepsy and the spectrum of epileptic phenotypes potentially ranged from the mild form of epilepsies such as absence epilepsy or partial epilepsy, to the severe form of developmental epileptic encephalopathy. The clinical phenotypes variability is potentially associated with the molecular sub-regional of the mutations.

Breast cancer is one of the most deadly malignancies in women worldwide. Salvia miltiorrhiza, a perennial plant that belongs to the genus Salvia, has long been used in the management of cardiovascular and cerebrovascular diseases. The main anti-breast cancer constituents in S. miltiorrhiza are liposoluble tanshinones including dihydrotanshinone I, tanshinone I, tanshinone IIA, and cryptotanshinone, and water-soluble phenolic acids represented by salvianolic acid A, salvianolic acid B, salvianolic acid C, and rosmarinic acid. These active components have potent efficacy on breast cancer in vitro and in vivo. The mechanisms mainly include induction of apoptosis, autophagy and cell cycle arrest, anti-metastasis, formation of cancer stem cells, and potentiation of antitumor immunity. This review summarized the main bioactive constituents of S. miltiorrhiza and their derivatives or nanoparticles that possess anti-breast cancer activity. Besides, the synergistic combination with other drugs and the underlying molecular mechanisms were also summarized to provide a reference for future research on S. miltiorrhiza for breast cancer treatment.

Very high requirement of co-registration accuracy better than 0.001 pixels for Sentinel-1 TOPS (Terrain Observation by Progressive Scan) mode presents a great challenge for application of SAR (synthetic aperture radar) interferometry in Earth Observation. The state-of-the-art techniques have demonstrated that the low coherence scenarios and abrupt loss of coherence between two arbitrary acquisitions are main sources of error to degrade the performance of TOPS time-series co-registration. In this paper, we present a modification to overcome both limitations through the coherence enhancement. The motive behind this is to improve the quality of observations before co-registration and meanwhile avoiding the coherence loss caused by fast decorrelation. To this end, principal components analysis based spatio-temporal filtering is first used to remove the artifacts in burst interferograms over strong noise areas. Rather than heuristically choosing a sub-set of interferograms as a small baseline technique does, we use Dijkstra's shortest path algorithm under graph theory framework to maximize the coherence of a sub-set interferograms. The performance of presented method against the state-of-the-art techniques is fully evaluated by synthetic data and a Sentinel-1A stack over a low coherence scene in Indonesia. Comprehensive comparisons demonstrate 9%-17% uncertainty reduction of time-series co-registration when applying our method.

On 1 May 2020, the Sardoba Reservoir in Uzbekistan breached its western wall, and the uncontrolled release of water caused casualties, environmental damages and economic losses. We investigate the dam failure based on three sets of Earth observation data, including: (i) satellite altimetry products, i.e., ICESat-2 data, with the aim of understanding the topographic features in the study area; (ii) multi-geometry Sentinel-1 SAR data to retrieve the pre-failure deformation along the vertical and horizontal east-west directions between 2017 and 2020; (iii) optical images from Sentinel-2 satellites and Global Precipitation Measurement (GPM) products, which are involved in exploring the environmental status before the failure. We analyse the possible causes of the collapse in terms of both physical and human factors. The differential settlement of ∼60 mm revealed by InSAR at the failure section is a sign of internal erosion through the embankment, which is the physical factor contributing to the failure. The opportunity to prevent the collapse were missed due to the human factor of ignorance brought by limitations of the conventional monitoring methods. Neither ground observations nor satellite-based GPM products show extreme precipitation in the region, ruling out the likelihood of rainfall-induced overtopping. The settlement rate of the embankment shows no decaying trend, indicating that the dam is undergoing the primary consolidation phase of total settlement. Maximum settlement of ∼270 mm (∼0.8% of the dam height) has happened on the north bank since the reservoir impoundment, which should raise concern in future monitoring and surveillance. The results reveal that InSAR can discern the failure precursor by detecting surface motion, and that the deformation signals can help to warn of risks and avoid dam damage. We recommend InSAR deformation monitoring be included in future safety programs, providing detailed deformation and resisting risks of ignorance.

Hepatocellular carcinoma (HCC) is one of the most lethal malignancies with extremely poor prognosis. Therefore, revealing the critical molecules involved in HCC progression and prognosis is urgently needed. In this study, through combining public dataset and our cohort, we found a novel prognosis-related long non-coding RNA KB-68A7.1 in HCC. KB-68A7.1 was lowly expressed in HCC, whose low expression was associated with large tumour size, aggressive clinical characteristic, and poor survival. Gain- and loss-of-function assays demonstrated that KB-68A7.1 restricted HCC cellular proliferation, induced HCC cellular apoptosis, and suppressed HCC cellular migration and invasion in vitro. Xenograft assays demonstrated that KB-68A7.1 suppressed HCC tumour growth and metastasis in vivo. These functional assays suggested KB-68A7.1 as a tumour suppressor in HCC. Histone methyltransferase nuclear receptor binding SET domain-containing protein 1 (NSD1) was found to bind to KB-68A7.1. KB-68A7.1 was mainly distributed in the cytoplasm. The binding of KB-68A7.1 to NSD1 sequestrated NSD1 in the cytoplasm, leading to the reduction in nuclear NSD1 level. Through decreasing nuclear NSD1 level, KB-68A7.1 reduced di-methylation of histone H3 at lysine 36 (H3K36me2) and increased tri-methylation of histone H3 at lysine 27 (H3K27me3) at the promoter of WNT10B, a target of NSD1. Thus, KB-68A7.1 repressed WNT10B transcription. The expression of WNT10B was negatively correlated with that of KB-68A7.1 in HCC tissues. Through repressing WNT10B, KB-68A7.1 further repressed Wnt/β-catenin signalling. Functional rescue assays showed that overexpression of WNT10B reversed the tumour-suppressive roles of KB-68A7.1, whereas the oncogenic roles of KB-68A7.1 depletion were abolished by Wnt/β-catenin signalling inhibitor. Overall, this study identified KB-68A7.1 as a lowly expressed and prognosis-related lncRNA in HCC, which suppressed HCC progression through binding to NSD1 and repressing Wnt/β-catenin signalling.

Forests are crucial in carbon sequestration and oxygen release. An accurate assessment of forest carbon storage is meaningful for Chinese cities to achieve carbon peak and carbon neutrality. For an accurate estimation of regional-scale forest aboveground carbon density, this study applied a Sentinel-2 multispectral instrument (MSI), Advanced Land Observing Satellite 2 (ALOS-2) L-band, and Sentinel-1 C-band synthetic aperture radar (SAR) to estimate and map the forest carbon density. Considering the forest field-inventory data of eastern China from 2018 as an experimental sample, we explored the potential of the deep-learning algorithms convolutional neural network (CNN) and Keras. The results showed that vegetation indices from Sentinel-2, backscatter and texture characters from ALOS-2, and coherence from Sentinel-1 were principal contributors to the forest carbon-density estimation. Furthermore, the CNN model was found to perform better than traditional models. Results of forest carbon-density estimation validated the improvements effectively by combining the optical and radar data. Compared with traditional regression methods, deep learning has a higher potential for accurately estimating forest carbon density using multisource remote-sensing data.

Can the antiferromagnetic (AF) order be induced via the local moments' hybridization with the heavy electrons instead of conduction electrons? We address this intriguingly fundamental question via a prototypical model to describe the interplay between local moments and heavy electrons. We discover that the AF order can be mediated via the heavy electrons through the mechanism of the high-order Ruderman-Kittel-Kasuya-Yosida (RKKY) interaction. Moreover, our analysis employing a slave-spin representation uncovers that the heavy electron band can enhance the RKKY interaction of local moments (despiteits high-order nature) via enlarging its effective mass. The potential relevance to the heavy fermion compound Ce_{3}(Pt/Pd)In_{11} is also discussed.

Land subsidence has been a significant problem in land reclaimed from the sea, and it is usually characterized by a differential settlement pattern due to locally unconsolidated marine sediments and fill materials. Time series Synthetic Aperture Radar Interferometry (InSAR) techniques based on distributed scatterers (DS), which can identify sufficient measurement points (MPs) when point-wise radar targets are lacking, have great potential to measure such differential reclamation settlement. However, the computational time cost has been the main drawback of current distributed scatterer interferometry (DSI) for its applications compared to the standard PSI analysis. In this paper, we adopted an improved DSI processing strategy for a fast and robust analysis of land subsidence in reclaimed regions, which is characterized by an integration of fast statistically homogeneous pixel selection based (FaSHPS-based) DS detection and eigendecomposition phase optimization. We demonstrate the advantages of the proposed DSI strategy in computational efficiency and deformation estimation reliability by applying it to two TerraSAR-X image data stacks from 2008 to 2009 to retrieve land subsidence over two typical reclaimed regions of Hong Kong International Airport (HKIA) and Hong Kong Science Park (HKSP). Compared with the state-of-the-art DSI methods, the proposed strategy significantly improves the computational efficiency, which is enhanced approximately 30 times in DS identification and 20 times in phase optimization. On average, the DSI strategy results in 7.8 and 3.7 times the detected number of MPs for HKIA and HKSP with respect to persistent scatter interferometry (PSI), which enables a very detailed characterization of locally differential settlement patterns. Moreover, the DSI-derived results agree well with the levelling survey measurements at HKIA, with a mean difference of 1.87 mm/yr and a standard deviation of 2.08 mm/yr. The results demonstrate that the proposed DSI strategy is effective at improving target density, accuracy and efficiency in monitoring ground deformation, particularly over reclaimed coastal areas.

The adaptive Goldstein filter driven by InSAR coherence is one of the most famous frequency domain-based filters and has been widely used to improve the quality of InSAR measurement with different noise features. However, the filtering power is biased to varying degrees due to the biased coherence estimator and empirical modelling of the filtering power under a given coherence level. This leads to under- or over-estimation of phase noise over the entire dataset. Here, the authors present a method to correct filtering power on the basis of the second kind statistical coherence estimator. In contrast with regular statistics, the new estimator has smaller bias and variance values, and therefore provides more accurate coherence observations. In addition, a piece-wise function model determined from the Monte Carlo simulation is used to compensate for the nonlinear relationship between the filtering parameter and coherence. This method was tested on both synthetic and real data sets and the results were compared against those derived from other state-of-the-art filters. The better performance of the new filter for edge preservation and residue reduction demonstrates the value of this method.