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Accurate disparity estimation of high-resolution satellite remote sensing stereo images serves as a crucial method for generating precise digital surface models. However, the complex intractable regions in satellite images (textureless regions, repeated texture regions, occlusion regions) pose serious challenges for accurate disparity estimation. To enhance the matching accuracy within intractable regions, a dual branch multiscale stereo matching network for high-resolution satellite stereo images is proposed. First, a dual branch feature extraction module is designed, which can perform efficient downsampling. This module can enhance the scene awareness capability of the model, enabling it to extract multiscale feature maps and construct multiscale cost volumes. Then, the cost aggregation process is executed in a coarse-to-fine manner. The method employs a simple hourglass structure and leverages low-scale information to guide the aggregation of high-scale cost volumes. Next, a disparity-channel attention mechanism is proposed for the cost aggregation process to obtain more representative feature information. Finally, a simple disparity refinement module is designed by utilizing both intensity and gradient information of the left image to improve the local details of the disparity map. Experiments are performed separately on the GaoFen-7 and US3D datasets. The experimental results indicate that the proposed method is conducive to improving the matching accuracy within intractable regions of satellite images. The structure of the proposed network is simple, which can effectively reduce the network parameters and realize the lightweight of the model.

This article presents an innovative geometric calibration mode for a linear array camera used in the Jilin-1 satellite. Changing the camera from observing the ground to observing space and treating stars as control points allow for the accurate determination of the geometric parameters of the camera. In this article, several important calibration procedures and strategies are presented to gradually eliminate the influence of astronomical and attitude errors. Self-verification of the calibration image results in a star-based calibration accuracy is better than 0.3 pixels. Further verification of the star image that leads to an interior orientation accuracy is better than 0.6 pixels. The accuracy of geometric positioning without control is verified by ground images of different regions to be 30 m. The interior orientation accuracy of ground images processed by star-based calibration parameters is better than 1.5 m. The ground image corrected using the star-based calibration parameters is well overlaid on the reference image. The accuracies obtained with star-based and ground-based calibrations are comparable. However, ground calibration fields require high maintenance costs and may suffer from harsh imaging conditions. Contrarily, star-based calibration is not affected by the weather and climate conditions on the ground, which can be photographed from any location along the satellite orbit. Therefore, star-based calibration can meet the requirements for high-frequency geometric calibration of subsequent satellite constellations construction.

The late spring cold spell severely affects the growth of litchi flower buds. Melatonin, as a signaling molecule, can enhance the plant’s ability to resist abiotic stress by regulating multiple physiological processes. However, there are few studies on the function of melatonin in litchi under cold stress. In the present study, 100 μM of melatonin was selected based on the ABA content in litchi seedlings. To identify genes potentially involved in melatonin and cold stress conditions in litchi, four RNA-seq libraries of litchi leaves under melatonin and cold conditions were constructed. In total, 6.4–8.5 Gb of trimmed bases were generated in each library. Thirty-five genes were randomly selected for qRT-PCR analysis. The results showed a strong positive correlation between the data from qRT-PCR and RNA-seq. A total of 4590 differentially expressed genes (DEGs) were identified in the treatment of melatonin (1845) and melatonin in cold condition (2745). The expression of several genes belonging to starch and sucrose metabolism, plant hormones (auxin, ABA), MAPK, and alpha-linolenic acid metabolism pathways were differentially expressed. The enhanced carbohydrate metabolism might lead to litchi seedlings treated with melatonin to produce more metabolic energy. Abscisic acid can improve cold tolerance. Collectively, our results reveal that pretreatment with melatonin (100 μM) protects litchi seedlings from cold stress through plant hormones and carbohydrate metabolism and provides potential genes for future research.

【Objective】Endophytic bacteria exert an important role in plant growth and development. The study aims to analyze the community structure and diversity of endophytic bacteria in pulp of different litchi varieties, and screen and utilize the potential endophytic bacteria resources, which would provide references for further research on the interaction between litchi and endophytic bacteria.【Method】The pulp tissues of 6 different litchi varieties were used as materials. MiSeq high-throughput sequencing technology was used to compare and analyze the community composition and dominant flora of endophytic bacteria at phylum, family, genus and species classification levels in pulp tissues after the V3~V4 region of 16S rDNA was amplified by PCR. The diversity of endophytic bacteria was evaluated combined with Alpha diversity analysis.【Result】A total of 2 139 086 sequences of endophytic bacteria 16S rDNA were obtained in the V3~V4 region of 6 litchi varieties, and the average length of amplified sequences was 376 bp. There was no significant difference in the abundance and diversity of endophytic bacteria in different litchi varieties. At the phylum classification level, Proteobacteria was the main dominant bacterial group in litchi pulp tissue, followed by Actinomyces. At the levels of family, genus and species classification, the endophytic bacterial community structure of different litchi varieties was similar, but the bacterial abundance proportion was different. Differential bacterial community analysis showed that the Pseudomonas, Pectobacterium, Ochrobactrum and Dickeya were significantly enriched in the high-quality varieties of 'Edanli', 'Nuomici' and 'Guanyinlv'. In addition, Bradyrhizobium and Snodgrassella were only enriched in the 'Edanli'. The clustering analysis of relative abundance of bacteria showed that 'Guanyinlv', 'Nuomici' and 'Edanli', had higher similarity. Alpha diversity analysis showed that there were differences in the abundance and diversity of endophytic flora in pulps of 6 litchi varieties, but the differences were not significant.【Conclusion】In this study, the community structure of endophytic bacteria in the pulp of six different litchi varieties was analyzed, and some unique microbial genera were enriched in the high-quality litchi varieties. The results could provide a theoretical basis and important references for the development and utilization of endophytic bacteria to improve litchi quality.

Image sharpness assesses detail visibility in remote-sensing images and measures sensors' details resolution capability. Sensor aging and environmental changes can degrade image sharpness and quality. The Gaofen (GF) satellites provide diverse remote-sensing imagery, but evaluations of their sharpness are limited. In this study, for the GF1/2/6/7 optical remote-sensing satellites in the space-based system of the China High-Resolution Earth Observation System (CHEOS) major special project, we evaluated the relative edge response (RER), full width at half maximum (FWHM), and modulation transfer function (MTF) of the images, using nearly ten years of ground target image data. This measures image sharpness and models how it changes over time with different sensors. Within ten years of on-orbit operation, the RER and MTF (@Nyquist frequency) of GF1/2 are 0.51 and 0.50, and 0.15 and 0.11, respectively. This indicated good image edge and high-frequency detail responsiveness, with FWHM of 1.16 and 1.17, respectively, showing a slight image sharpening. For GF6, the RER, MTF (@Nyquist frequency), and FWHM were 0.42, 0.09, and 1.39, indicating improved sharpening compared with GF1/2 but decreased edge and high-frequency detail response. The RER, MTF (@Nyquist frequency), and FWHM of the panchromatic images of GF7 were 0.32, 0.04, and 1.91, which indicate image blur. Meanwhile, the corresponding indicators for the multispectral images were 0.45, 0.14, and 1.40, better than the panchromatic images. Long-term data showed periodic sharpness variations in satellite images, with GF6s stability and minimal track differences being superior. The dynamic change pattern corresponds to a fourth-order polynomial model.

The ice, cloud, and land elevation satellite-2 (ICESat-2) is equipped with an advanced topographic laser altimeter system (ATLAS) that obtains small spots and high-density photon data. ATLAS has the potential for high-precision detection of global surfaces and helps calculate global carbon sinks. One important carbon sink includes forests and making it critical to precisely measure their carbon storage and obtain both ground and top-of-canopy (TOC) levels of vegetation. Here, we explored a precise and highly reliable ground and TOC detection method based on ICESat-2 data to accurately remove noise and classify photons. This method can be adapted to different scenes and topographies. The first step included the removal of pronounced dispersion noise and pseudosignal photon aggregation noise. We introduced two algorithms for coarse noise removal—an adaptive threshold ellipsoid filter algorithm and an outlier photon cluster removal algorithm based on the photon group distance. Then, the local outlier factor algorithm was modified to remove the noise of near-signal photons to obtain the result of fine denoising. Finally, the modified local direction center algorithm was combined with the uniaxial inverse distance-weighted statistics to distinguish the ground from the TOC. Across varied scenes and terrains, the proposed denoising method demonstrated an overall accuracy exceeding 0.94. Additionally, the root-mean-square error of the ground and TOC obtained via the classification method was under 1.4 m and 3.2 m, respectively. These findings highlight that this method has a high level of robustness in effectively detecting both the ground and TOC.

Most existing road extraction methods prioritize region accuracy at the expense of ignoring road boundaries and connectivity quality. The occluding objects such as buildings, trees, and vehicles in remote sensing data usually cause discontinuous mask outputs, and consequently affect road extraction accuracy. In this article, a road extraction fusion network perceiving region and boundary features is proposed. The combination of a location-aware transformer and convolutional neural network is responsible for focusing regional semantic information through adaptive weight filtering. Combining spatial and channel information, the graph convolutional network is improved by constructing an integrated adjacency matrix to consider the relationships between nodes at different scales, which allows for better capture of multiscale contexts. Boundary details are used to complement regional features, thereby enhancing the connectivity of masks. Comprehensive quantitative and qualitative experiments demonstrate that our method significantly outperforms state-of-the-art methods on two public benchmarks, which can improve road extraction by handling interruptions related to shadows and occlusions, producing high-resolution masks.

Hyperspectral imagery with high spatial resolution (H2) imagery can synchronously obtain the spectral and spatial features of objects, thus providing richer information. However, the exacerbated spatial–spectral heterogeneity poses new challenges for classification. In this study, an interactive learning framework was proposed to address the current issues in H2 imagery classification. Specifically, we propose a spectral–spatial purification network (S2PNet) to improve classification accuracy. First, a multistage spectral purification module is designed to purify noisy information and mitigate spectral heterogeneity, achieving interaction between spectral optimization and classification. Second, a global–local mutual guide module is utilized to realize image–pixel-level feature interaction, thus enhancing the spatial discriminability of extracted features and reducing spatial heterogeneity. Third, the introduction of dual-stream semantic progressive module facilitates shallow-deep feature interaction, reducing the semantic gap in internal network and enabling a smoother information flow. We validated our approach using the public WHU-Hi hyperspectral datasets and large-scale Houston datasets. Experimental results demonstrate that S2PNet achieves the highest classification accuracy across all tests, significantly outperforming state-of-the-art methods.

Earth observation utilizes multisource satellite data to enhance photogrammetry mapping. This study introduces a novel method to improve the geometric positioning accuracy of large-scale optical satellite imagery by combined adjustment with NASA's Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) laser altimetry data. Although ICESat-2 is known for its high vertical accuracy, its potential to improve horizontal accuracy has been limited due to the difficulty in matching caused by temporal inconsistencies across large survey areas. To address this, our method employs a robust terrain profile elevation sequence similarity matching technique, refined with two-dimensional Gaussian fitting to achieve enhanced position extraction. We also propose a weighted adjustment strategy that uses matching confidence to enhance the precision of the combined adjustments. Large-scale tests across various terrains showed that our approach has significantly reduced horizontal and vertical positioning errors to 4.3 and 1.7 m, respectively, outperforming existing methods.

With the continuous increase in train running speeds and the rapid complexity of operation environments, running stability of the high-speed train is facing significant challenges. A series of abnormal vibration issues, caused by hunting instability, have emerged, including bogie instability alarm, carbody swaying, and carbody shaking, posing a significant threat to the safe and stable operation of high-speed trains. Therefore, the monitoring and diagnosis of hunting instability have become important research topics in rail transit. This review follows the development of fault diagnosis for bogie hunting instability and carbody hunting instability. It first summarizes the existing evaluation standards and innovative diagnostic methods. Due to the current limitation of hunting instability evaluation standards, which can only detect large-amplitude hunting, this paper addresses the gap in evaluation criteria for early-stage, small amplitude hunting instability diagnosis. A thorough overview of the progress made by researches in this field of research is given, emphasizing three primary facets: diagnostic signal sources, diagnostic features, and diagnostic targets. Furthermore, given that existing methods only classify faults into small and large amplitudes, which does not meet the practical need for quickly and accurately identifying fault types and severity during operation, this review introduces existing works on the detailed assessment and fault tracing of hunting instability, as well as the mechanisms underlying its occurrence, with the aim of achieving a comprehensive diagnosis of hunting instability. Finally, the limitations of current methods and the future development trends in hunting instability diagnostics are discussed and summarized. This paper provides readers with a framework for the research process of hunting instability diagnosis, offering valuable references and innovative perspectives for their future research efforts.

The feedback regulatory effects of estrogen (E2) and androgen (T) on the gonadotropin-releasing hormone (GnRH) and gonadotropin (GtH) within the brain–pituitary–gonad (BPG) axis in eels with undeveloped ovaries were investigated through in vivo studies. However, the regulatory role of the BPG axis only became apparent during ovary development in the migratory stage. To further elucidate the direct feedback regulation of the BPG axis, female Anguilla japonica underwent artificial induction of vitellogenesis, and the regulation of BPG axis tissues by GtH (human chorionic gonadotropin, hCG), E2, and T was explored through in vitro exposure. The mRNA expression levels of GnRH (mGnRH), GtH (fshb and lhb), and steroid biosynthesis enzymes (cyp11a1, hsd3b, cyp17a1, and cyp17a2) in the diencephalon, pituitary, and ovary, respectively, were determined. The results showed that the expression level of mGnRH in the diencephalon was significantly downregulated by 0.1 IU/mL hCG but upregulated by both 1 nM E2 and higher concentrations of T, suggesting a direct positive feedback regulation of E2 on mGnRH. In the pituitary, the expression levels of fshb and lhb were upregulated by E2, while fshb was suppressed by T. In the ovaries, the expression of cyp11a1 and hsd3b was upregulated by 1 nM E2, whereas T exposure resulted in an opposite effect. Cyp17a1 mRNA levels did not differ significantly with E2 treatment but were upregulated by 1 nM T. These findings suggest that low concentrations of E2 exhibited positive feedback regulation on all three levels (diencephalon, pituitary, and ovary) of the BPG axis, while T showed weaker and differential feedback regulation in BPG axis tissues. Overall, this study’s results revealed the direct feedback regulation of hCG, E2, and T on the BPG axis in eels, a phylogenetic base of teleosts.

Building height (BH) estimation is crucial for urban spatial planning and development. BH estimation using digital surface model data involves obtaining ground and roof elevations. However, vegetation and shadows around buildings affect the selection of the required elevation, resulting in large BH estimation errors. In highly urbanized areas, buildings of similar heights often have similar characteristics and spatial proximity, which have reference significance in BH estimation but are rarely utilized. Herein, we propose a BH estimation method based on BIRCH clustering and a random forest (RF) model. We obtain the initial BH results using a method based on the optimal ground search area and a multi-index evaluation. BIRCH clustering and an RF classification model are used to match buildings of similar heights based on their spatial distance and attribute characteristics. Finally, the BH is adjusted based on the ground elevation obtained from the secondary screening and the BH matching. The validation results from two areas with over 12,000 buildings show that the proposed method reduces the root-mean-square error of the final BH results compared with the initial results. Comparing the obtained height maps shows that the final results produce a relatively accurate BH in areas with high shading and vegetation coverage, as well as in areas with dense buildings. Thus, the proposed method has been validated for its effectiveness and reliability.

【Objective】Xianjinfeng litchi is a new variety introduced in recent years. It is characterized by high quality and late ripening and has significant economic and social benefits in the middle and late ripening litchi producing areas, however, its efficient and high-yield cultivation technology needs to be studied. Shoot bending is an important shaping and pruning method in orchard management, which can effectively reduce the apex dominance and facilitate flowering and fruiting. Here, the effects of different bending treatments on Xianjinfeng litchi were explored in order to provide references for the application of shoot bending technology as well as the establishment of technology for stable and high yield.【Method】Xianjinfeng litchi grafted for four years was used as the test material, and three bending treatments were set: vertical (0°, CK), horizontal (90 °) and drooping (110 °). After treatments, the indicators including growth, flowering rate, and fruit setting rate were counted.【Result】Compared with the vertical treatment (CK), drooping and horizontal treatments had limited effects on the vegetative growth of the first and second shoots, but delayed the germination of the third shoot (the last autumn shoot) for more than seven days, and dramatically reduced germination number of shoots at the same time. In addition, drooping treatment had the strongest inhibitory effect. When the leaves of the last autumn shoot were mature, the branch diameter under bending treatments was slightly lower than that under CK, however, and there were no significant difference among all treatments. Both drooping and horizontal bending treatments could effectively control the length of the last autumn shoot to about 15 cm, promote the growth of short and medium shoots, which was conducive to flowering and fruiting. In the coming spring, the flowering rate and fruit setting rate of drooping and horizontal treatments were notably higher than those of vertical treatment (CK). The flowering rate of droop treatment reached 100%, and all of them were pure flowers with high quality, while vertical treatment (CK) produced more panicles with leaves in comparison with the other two treatments. There was no significant difference in the length of panicle among different treatments, however, the base diameter of panicle were observably reduced and vigorous growth of panicles were inhibited by bending treatments. Fifty days after blooming of pistillate flowers, the fruit setting rates under drooping and horizontal treatments were 9.81% and 7.61%, respectively, markedly higher than that under vertical treatment (CK, 5.54%).【Conclusion】For Xianjinfeng litchi, drooping and horizontal branch bending can effectively control the excessive growth of parent branches, improve the quality of flowers, and significantly increase fruit setting rate. The best results are obtained under drooping treatment (110°).

Temporal-based methods effectively improve the utilization rate of remote sensing images but large ratios of missing information still need to be improved in the reconstruction models. In this article, based on the imaging theory with the help of a radiation correction model, a decoupling-reconstruction network (DecRecNet) for image reconstruction is proposed. The network uses a ground content radiation (GCR) correction module and imaging environment radiation (IER) correction module and their corresponding loss functions to decouple the image radiation information into GCR related to the ground objects and IER related to the imaging conditions and carry out targeted processing to achieve the purpose of missing information reconstruction. The GCR consistency loss function is used to preserve the ground content information, and the IER consistency loss function and IER smoothness loss are used to coordinate the imaging environment. The radiation-guiding module further performs targeted radiation adjustment on the foreground and background images to transfer the background imaging environment to the foreground and consistent radiation information of the same ground object. Compared with the classical U-Net, DeepLabV3+, RFR-Net, and spatial-temporal-spectral deep convolutional neural network methods, our model showed remarkable advantages in cloud occlusion and stripes of Landsat-8 (30 m), GaoFen-1 (2 m), and Landsat-7 (30 m) at various missing ratios, data sources, resolutions, and scenes, and achieving the goal of missing information reconstruction in large missing ratios.

Abstract The product functions (PFs) extracted by local mean decomposition (LMD) of the noisy signal contain obvious energy‐concentrated pulses. As a result, the conventional amplitude threshold filtering used in wavelet transform (WT)‐based and empirical mode decomposition (EMD)‐based de‐noising methods is no longer applicable. To address this issue, an improved signal de‐noising method is proposed by using the multi‐level local mean decomposition (ML‐LMD), the superposition and recombination (SR) of high‐order PFs, the outlier detection, and waveform smoothing (OD‐WS) to remove noise by eliminating the pulse components. The proposed method's superior noise reduction performance is demonstrated through theoretical analysis and experimental verification. Compared to well‐known methods like WT‐based and EMD‐based de‐noising, the results show that the proposed method has significant comparative advantages in reducing noise in rolling bearing signals.

Suitable reference genes are one of the necessary conditions for obtaining reliable results by real-time fluorescence quantitative PCR (qPCR). In this study, the expression of the 10 common candidate reference genes (18s rRNA, rps27, cnbp, rpl7, ube2, hsp-at, gapdh, β-actin, rpl13a, 1-ef1a) at different developmental stages of gonad and embryo of Takifugu bimaculatus were analyzed by qPCR. And the expression stability of these reference genes is analyzed by GeNorm, NormFinder and Bestkeeper softwares. The results showed that the expression stability of 1-ef1a was the highest (p

The era of big data has arrived, and it has penetrated every aspect of human life.As the geo-spatial information science spawned by the intersection of earth sciences and information sciences, the advent of the era of big data provides it with richer prosperous data protection, but also brings new challenges in data storage, management, analysis, and mining, and even caused a certain degree of “data explosion”.From the perspective of big data, the bottlenecks and challenges in the four core areas of geographic information systems, smart cities, remote sensing big data, and spatial data mining were sorted out.And it was pointed out that geo-spatial information science can provide more accurate and real-time spatial information frameworks and more intelligent and more efficient information processing methods for geoscience research, serving intelligent cities, smart earth construction, and sustainable development in the era of big data.Moreover, in the era of big data, the development of geo-spatial information science is facing the double test of software and hardware levels.

Background: The association between immune imbalances and adverse pregnancy outcomes has been extensive investigated by observational studies, but remain unclear. Thus, this study aimed to establish the causality of the circulation levels of cytokines on adverse pregnancy outcomes, such as offspring’s birthweight (BW), preterm birth (PTB), spontaneous miscarriage (SM), and stillbirth (SB).Methods: Two-sample Mendelian randomization (MR) analysis was employed to investigate potential causal relations between 41 cytokines and pregnancy outcomes on the basis of previously published GWAS datasets. Multivariable MR (MVMR) analysis was implemented to investigate the effect of the composition of cytokine networks on the pregnancy outcomes. Potential risk factors were further estimated to explore the potential mediators.Results: Genetic correlation analysis based on large GWAS data sources revealed that genetically predicted MIP1b (β = −0.027, S.E. = 0.010, p = 0.009) and MCSF (β = −0.024, S.E. = 0.011, p = 0.029) were associated with reduced offspring’s BW, MCP1 (OR: 0.90, 95% CI: 0.83–0.97, p = 0.007) was associated with reduced SM risk, SCF (β = −0.014, S.E. = 0.005, p = 0.012) associated with decreased number of SB in MVMR. The univariable MR showed that GROa (OR: 0.92, 95% CI: 0.87–0.97, p = 0.004) was associated with decreased PTB risk. Except for the MCSF-BW association, all above associations surpassed the Bonferroni corrected threshold. The MVMR results revealed that MIF, SDF1a, MIP1b, MCSF and IP10 composed cytokine networks, associated with offspring’s BW. Risk factors analysis indicated that the above causal associations might be mediated by smoking behaviors.Conclusion: These findings suggest the causal associations of several cytokines with adverse pregnancy outcomes, which were potentially mediated by smoking and obesity. Some of the results did not been corrected through multiple tests and larger samples verification is required in further studies.

Recently, bearing fault diagnosis methods based on deep learning have achieved significant success. However, in practical engineering applications, the limited labeled data and various working conditions severely constrain the widespread application of most deep-learning-based fault diagnosis methods. Additionally, many methods focus solely on the amplitude information of samples, neglecting the rich relational information between samples. To address these issues, this paper proposes a novel cross-condition few-shot fault diagnosis method based on an adaptive dynamic threshold graph neural network (ADTGNN). The aim of the proposed method is to rapidly identify fault types after they occur only a few times or even once. The adaptive threshold computation module (ATCM) in ADTGNN dynamically assigns thresholds to each edge based on edge confidence, optimizing the graph structure and effectively alleviating the over-smoothing issue. Furthermore, a dynamic threshold adjustment strategy (DTAS) is introduced to gradually increase the threshold with the training iterations, preventing the model from prematurely discarding crucial edges due to insufficient performance. The proposed model’s effectiveness is demonstrated using three bearing datasets. The experimental results indicate that the proposed approach significantly outperforms other comparison methods in cross-condition bearing fault diagnosis.

Large yellow croaker Larimichthys crocea (Richardson) is an important member in family Sciaenidae, and one of most productive mariculture fishes in China. Fluorescence in situ hybridization is a useful tool for cytogenetics and genomics research. Here, we demonstrated that bacterial artificial chromosome (BAC) clones could be used to identify individual chromosomes in large yellow croaker, and also to study chromosome evolution in the related species. By using BAC paired-end sequencing and sequence alignment, 435 BAC clones were anchored to 24 pseudochromosomes of large yellow croaker genome. Among them, 72 BAC clones with low repeat content were selected and passed PCR test, and then grouped by chromosome for FISH test. As a results, there were 67 BAC clones, 2 to 3 BAC clones per chromosome, generated specific and stable signal at expected position. Then, a dual-color FISH probe cocktail composed of 48 of these BAC clones was formulated and used to hybridize metaphase chromosome spreads, resulting in distinct signal patterns on each chromosome pair, which help to distinguish all chromosomes in the metaphase spreads of large yellow croaker. The chromosome-specific BAC-FISH probes were also applied to a close relative of large yellow croaker, Collichthys lucidus, demonstrating that its Y chromosome originated from the fusion of Chr.1 and Chr.7. Thus, our study provides the first set of chromosome-specific FISH probes in family Sciaenidae, which will play an important role in cytogenetics and genomics research in the family.

Human fetal adrenal glands produce substantial amounts of dehydroepiandrosterone (DHEA), which is one of the most important precursors of sex hormones. However, the underlying biological mechanism remains largely unknown. Herein, we sequenced human fetal adrenal glands and gonads from 7 to 14 gestational weeks (GW) via 10× Genomics single-cell transcriptome techniques, reconstructed their location information by spatial transcriptomics. Relative to gonads, adrenal glands begin to synthesize steroids early. The coordination among steroidogenic cells and multiple non-steroidogenic cells promotes adrenal cortex construction and steroid synthesis. Notably, during the window of sexual differentiation (8–12 GW), key enzyme gene expression shifts to accelerate DHEA synthesis in males and cortisol synthesis in females. Our research highlights the robustness of the action of fetal adrenal glands on gonads to modify the process of sexual differentiation.

The modulation transfer function (MTF) is a key characteristic used to assess the performance of optical remote sensing satellite sensors. MTF detection can directly measure a sensor’s two-dimensional (2D) point spread function (PSF); therefore, it has been applied to various high-resolution remote sensing satellites (e.g., Pleiades) using point sources. However, current point source methods mainly use 2D Gaussian functions to fit the discrete digital number (DN) of the point source on the image to extract the center of the point source and fit the PSF after encrypting multiple point sources; thus, noise robustness is poor and measurement accuracy varies widely. In this study, we developed a noise-resistant on-orbit MTF detection method based on the object space constraint among point source arrays. Utilizing object space constraint relationships among points in a point source array, a homography transformation model was established, enabling accurate extraction of sub-pixel coordinates for each point source response. Subsequently, aligning the luminosity distribution of all point sources concerning a reference point source, the encrypted PSF was obtained and then fitted to obtain the MTF. To validate the method, Gaofen-2 (GF-2) satellite images were used to conduct an in-orbit imaging experiment on the point source array of the Chinese Zhongwei remote sensing satellite calibration site. Compared with the Gaussian model methods, the proposed method yielded more accurate peak positions for each point source. Standard deviations of peak position constant ratios in along- and cross-track directions improved by 2.8 and 4.8 times, respectively. The root-mean-square error (RMSE) of the collinearity test results increased by 92%, and the noise resistance of the MTF curve improved by two times. Dynamic MTF values at the Nyquist frequency for the GF-2 panchromatic band in along- and cross-track directions were 0.0476 and 0.0705, respectively, and MTF values in different directions were well distinguished.

This paper introduces a novel approach for detecting inter-turn short-circuit faults in rotor windings using wavelet transformation and empirical mode decomposition. A MATLAB/Simulink model is developed based on electrical parameters to simulate the inter-turn short circuit by adding a resistor parallel to phase “a” of the rotor. The resulting high current in the new phase indicates the presence of the short circuit. By measuring the rotor and stator three-phase currents, the fault can be detected as the currents exhibit asymmetric behavior. Fluctuations in the electromagnetic torque also occur during the fault. The wavelet transform is applied to the rotor current, revealing an effective analysis of sideband frequency components. Specifically, changes in amplitude and frequency, particularly in d7 and a7, indicate the presence of harmonics generated by the inter-turn short circuit. The simulation results demonstrate the effectiveness of wavelet transformation in analyzing these frequency components. Additionally, this study explores the use of empirical mode decomposition to detect faults in their early stages, observing substantial changes in the instantaneous amplitudes of the first three intrinsic mode functions during fault onset. The proposed technique is straightforward and reliable, making it suitable for application in wind turbines with simple electrical inputs.

Lychee downy blight (LDB) is an oomycete pathogen (Peronophythora litchi) disease, which affects the fruits and leaves of lychee, resulting in economic losses. Chemical fungicides are commonly used for disease control, while for eco-safety measures, the study of molecular mechanisms involved in lychee resistance against LDB is necessary. Non-coding RNAs (ncRNAs), including long ncRNAs (lncRNAs), circular (circRNAs), and microRNAs (miRNAs), play a crucial role in plant disease resistance. To examine lychee response (fruits and leaves) to LDB, we studied the expression patterns of ncRNAs and mRNAs under LDB treatment. By whole transcriptome sequencing analyses, a total of 36,885 mRNAs, 2627 lncRNAs, 4682 circRNAs, and 525 miRNAs were identified in lychee. A differential expression (DE) analysis revealed that there were 1095 DEmRNAs, 89 DElncRNAs, 28 DEcircRNAs, and 28 DEmiRNAs in the LDB-treated fruits, as well as 1158 DEmRNAs, 132 DElncRNAs, 13 DEcircRNAs, and 197 DEmiRNAs in the LDB-treated leaves. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses revealed that the potential function of DEmRNAs and the targets of ncRNAs were involved in plant defense. Furthermore, co-expression networks of putative interacting ncRNAs and mRNAs were developed, in which mRNAs encoded some receptor proteins, pointing to potential ncRNAs associated with LDB infection. Our study provided a new, brief insight to the putative role of ncRNAs in lychee response to LDB.

Abstract Background Trachypithecus leucocephalus, the white-headed langur, is a critically endangered primate that is endemic to the karst mountains in the southern Guangxi province of China. Studying the genomic and transcriptomic mechanisms underlying its local adaptation could help explain its persistence within a highly specialized ecological niche. Results In this study, we used PacBio sequencing and optical assembly and Hi-C analysis to create a high-quality de novo assembly of the T. leucocephalus genome. Annotation and functional enrichment revealed many genes involved in metabolism, transport, and homeostasis, and almost all of the positively selected genes were related to mineral ion binding. The transcriptomes of 12 tissues from three T. leucocephalus individuals showed that the great majority of genes involved in mineral absorption and calcium signaling were expressed, and their gene families were significantly expanded. For example, FTH1 primarily functions in iron storage and had 20 expanded copies. Conclusions These results increase our understanding of the evolution of alkali tolerance and other traits necessary for the persistence of T. leucocephalus within an ecologically unique limestone karst environment.

Rolling element bearings are crucial, of high failure rate and easy damage parts of rotating machinery, and significantly affect safe and reliable production processes. Much more attention has been focused on fault diagnosis of rolling element bearings in recent years. This article presents a novel feature extraction scheme for the classification of multiple bearing faults. Multi-Scale Sample Entropy (M-SSampEn) is combined with Energy Moment (EM) to construct a time-domain Multi-Scale Sample Entropy-based Energy Moment (M-SSampEn-EM) feature extractor. The classifier model for the proposed fault classification system has been built using the Least Square Support Vector Machine (LS-SVM). The M-SSampEn-EM feature extractor is used to capture two-dimensional representative eigenvectors from multiple fault classes' bearing vibration data. Its separability performance is ensured by optimizing the feature-extraction parameters, including the Embedded Dimension and the Tolerance, etc. The LS-SVM classifier is also compared with two Neural Network (NN)-based classifiers, i.e., Radial Basis Function NN (RBFNN) and Probabilistic NN (PNN), to show it better generalization performance on bearing fault classification. The experimental study verifies the excellent capacity of the proposed approach in bearing fault classification.

Abstract Background Mental fatigue is usually caused by long-term cognitive activities, mainly manifested as drowsiness, difficulty in concentrating, decreased alertness, disordered thinking, slow reaction, lethargy, reduced work efficiency, error-prone and so on. Mental fatigue has become a widespread sub-health condition, and has a serious impact on the cognitive function of the brain. However, seldom studies investigate the differences of mental fatigue on electrophysiological activity both in resting state and task state at the same time. Here, twenty healthy male participants were recruited to do a consecutive mental arithmetic tasks for mental fatigue induction, and electroencephalogram (EEG) data were collected before and after each tasks. The power and relative power of five EEG rhythms both in resting state and task state were analyzed statistically. Results The results of brain topographies and statistical analysis indicated that mental arithmetic task can successfully induce mental fatigue in the enrolled subjects. The relative power index was more sensitive than the power index in response to mental fatigue, and the relative power for assessing mental fatigue was better in resting state than in task state. Furthermore, we found that it is of great physiological significance to divide alpha frequency band into alpha1 band and alpha2 band in fatigue related studies, and at the same time improve the statistical differences of sub-bands. Conclusions Our current results suggested that the brain activity in mental fatigue state has great differences in resting state and task state, and it is imperative to select the appropriate state in EEG data acquisition and divide alpha band into alpha1 and alpha2 bands in mental fatigue related researches.

Rolling bearings are critical in maintaining smooth operation of rotating machinery and considerably influence its reliability. The signals collected from rolling bearings in field conditions are often subjected to noise, creating a challenge to extract weaker fault features. This paper proposes a rolling bearing fault diagnosis method that addresses the above-mentioned problem through the moth-flame optimization algorithm optimized variational mode decomposition (MFO-optimized VMD) and an ensemble differential evolution online sequential extreme learning machine (DE-OSELM). By using the dynamic adaptive weight factor and genetic algorithm cross operator, the optimization accuracy and global optimization ability of the moth-flame optimization (MFO) are improved, and the two basic parameters of VMD decomposition level and quadratic penalty factor are adaptive selected. Since the vibration characteristics of the signal cannot be fully interpreted by a single index, The effective weighted correlation sparsity index (EWCS) is utilized to extract the relevant intrinsic mode functions (IMF) of VMD decomposition and extract their energies as features. In order to improve the classification accuracy, The energy feature set is subsequently inputted into DE-OSELM for training and classification purposes, and the proposed method is assessed via a sample set with four different health states of actual rolling bearings. Our proposed method results are compared with other diagnosis methods, proving its feasibility to diagnose rolling bearing faults with higher classification accuracy.

The integration of transcriptome and proteome analysis can lead to the discovery of a myriad of biological insights into ovarian cancer. Proteome, clinical, and transcriptome data about ovarian cancer were downloaded from TCGA’s database. A LASSO–Cox regression was used to uncover prognostic-related proteins and develop a new protein prognostic signature for patients with ovarian cancer to predict their prognosis. Patients were brought together in subgroups using a consensus clustering analysis of prognostic-related proteins. To further investigate the role of proteins and protein-coding genes in ovarian cancer, additional analyses were performed using multiple online databases (HPA, Sangerbox, TIMER, cBioPortal, TISCH, and CancerSEA). The final resulting prognosis factors consisted of seven protective factors (P38MAPK, RAB11, FOXO3A, AR, BETACATENIN, Sox2, and IGFRb) and two risk factors (AKT_pS473 and ERCC5), which can be used to construct a prognosis-related protein model. A significant difference in overall survival (OS), disease-free interval (DFI), disease-specific survival (DSS), and progression-free interval (PFI) curves were found in the training, testing, and whole sets when analyzing the protein-based risk score (p < 0.05). We also illustrated a wide range of functions, immune checkpoints, and tumor-infiltrating immune cells in prognosis-related protein signatures. Additionally, the protein-coding genes were significantly correlated with each other. EMTAB8107 and GSE154600 single-cell data revealed that the genes were highly expressed. Furthermore, the genes were related to tumor functional states (angiogenesis, invasion, and quiescence). We reported and validated a survivability prediction model for ovarian cancer based on prognostic-related protein signatures. A strong correlation was found between the signatures, tumor-infiltrating immune cells, and immune checkpoints. The protein-coding genes were highly expressed in single-cell RNA and bulk RNA sequencing, correlating with both each other and tumor functional states.

The primary cause for the limited shelf life of litchi fruit is rapid pericarp browning and decay. This study aims to evaluate the storability of 50 litchi varieties and establish a linear regression model for pericarp browning and decay based on 11 postharvest physical and chemical indices after 9 days of storage at room temperature. The results indicated that the average value of the browning index and decay rate significantly increased to 3.29% and 63.84% of 50 litchi varieties at day 9, respectively. Different litchi varieties showed different variations in appearance indicators, quality indicators, and physiological indicators. Furthermore, principal component analysis and cluster analysis revealed that Liu Li 2 Hao exhibited the highest resistance to storage, whereas Dong Long Mi Li, Jiao Pan Li, E Dan Li 2 Hao, and Ren Shan Li were not resistant. Stepwise multiple regression analysis further demonstrated that the factors were highly correlated with the decay index, with a partial correlation coefficient of 0.437 between the effective index and the decay index. Therefore, pericarp thickness, relative conductivity, pericarp laccase activity, and total soluble solids were significant indicators for the comprehensive evaluation of litchi browning and decay, and relative conductivity was the significant determinant causing fruit browning. These findings provide a new perspective on the sustainable development of the litchi industry.

The two video satellites of the second and third batch of Zhuhai-1 microsatellites (referred to as OVS-2A/3A) are operational with their hyperspectral satellites, which improves the data acquisi-tion capability of the Zhuhai-1 remote sensing satellite constellation. Contrary to the linear array push-broom hyperspectral satellites and plane array CCD video satellites, the OVS satellite is equipped with a planar array Bayer pattern sensor, which can obtain single-band grayscale images by push-broom imaging. Additionally, the Bayer color reconstruction algorithm can interpolate sensor data to provide RGB color band information. Therefore, for the Bayer pattern push-broom sensor, the relative calibration method of linear push-broom or array cameras cannot be directly applied. The radiometric calibration of the Bayer pattern push-broom imaging mode has become a matter of concern; therefore, this study developed a radiometric calibration method for the Bayer pattern push-broom sensor of the OVS satellite and verified its effectiveness and accuracy. OVS images were used to perform on-orbit relative radiometric calibration, and the calibration accu-racy, including streaking metrics and root-mean-square error, was better than 1%, meeting the specification requirements for the OVS satellite. Visually, after calibration correction, the streaking and striping noise of the Bayer images was removed, and the radiometric quality of the image was considerably improved, providing a good data basis for subsequent research in remote sensing applications.

Mechanical signals are not only disturbed by Gaussian noise, but also by non-Gaussian noise. These Gaussian noise and non-Gaussian noise have gravely impeded detecting of rolling bearing defects using traditional methods. In this context, the paper develops a novel detection method for rolling bearing, which combines bispectrum analysis with an improved ensemble empirical mode decomposition (EEMD). To effectively eliminate Gaussian noise in the signal, bispectrum analysis is adopted. In order to effectively reduce non-Gaussian noise, a cloud model-improved EEMD is proposed, where the cloud model is introduced to restrain the mode mixing phenomenon. Then a rolling bearing defect detection plan based on the proposed method is put forward. From theoretical analysis and experimental verification, it is demonstrated that the proposed method has superior performance in reducing multiple background noise. Furthermore, compared with other three methods, the results show that the proposed method can detect the defect of rolling bearings more effectively.

Abstract Background Phenomics provides new technologies and platforms as a systematic phenome-genome approach. However, few studies have reported on the systematic mining of shared genetics among clinical biochemical indices based on phenomics methods, especially in China. This study aimed to apply phenomics to systematically explore shared genetics among 29 biochemical indices based on the Fangchenggang Area Male Health and Examination Survey cohort. Result A total of 1999 subjects with 29 biochemical indices and 709,211 single nucleotide polymorphisms (SNPs) were subjected to phenomics analysis. Three bioinformatics methods, namely, Pearson’s test, Jaccard’s index, and linkage disequilibrium score regression, were used. The results showed that 29 biochemical indices were from a network. IgA, IgG, IgE, IgM, HCY, AFP and B12 were in the central community of 29 biochemical indices. Key genes and loci associated with metabolism traits were further identified, and shared genetics analysis showed that 29 SNPs (P

Abstract Background Chronic prostatitis has been supposed to be associated with preneoplastic lesions and cancer development. The objective of this study was to examine how chronic inflammation results in a prostatic microenvironment and gene mutation in C57BL/6 mice. Methods Immune and bacterial prostatitis mouse models were created through abdominal subcutaneous injection of rat prostate extract protein immunization (EAP group) or transurethral instillation of uropathogenic E. coli 1677 (E. coli group). Prostate histology, serum cytokine level, and genome-wide exome (GWE) sequences were examined 1, 3, and 6 months after immunization or injection. Result In the EAP and E. coli groups, immune cell infiltrations were observed in the first and last months of the entire experiment. After 3 months, obvious proliferative inflammatory atrophy (PIA) and prostatic intraepithelial neoplasia (PIN) were observed accompanied with fibrosis hyperplasia in stroma. The decrease in basal cells (Cytokeratin (CK) 5+/p63+) and the accumulation of luminal epithelial cells (CK8+) in the PIA or PIN area indicated that the basal cells were damaged or transformed into different luminal cells. Hic1, Zfp148, and Mfge8 gene mutations were detected in chronic prostatitis somatic cells. Conclusion Chronic prostatitis induced by prostate extract protein immunization or E. coli infection caused a reactive prostatic inflammation microenvironment and resulted in tissue damage, aberrant atrophy, hyperplasia, and somatic genome mutation.

Osteosarcoma (OS), which occurs most commonly in adolescents, is associated with a high degree of malignancy and poor prognosis. In order to develop an accurate treatment for OS, a deeper understanding of its complex tumor microenvironment (TME) is required. In the present study, tissues were isolated from six patients with OS, and then subjected to single-cell RNA sequencing (scRNA-seq) using a 10× Genomics platform. Multiplex immunofluorescence staining was subsequently used to validate the subsets identified by scRNA-seq. ScRNA-seq of six patients with OS was performed prior to neoadjuvant chemotherapy, and data were obtained on 29,278 cells. A total of nine major cell types were identified, and the single-cell transcriptional map of OS was subsequently revealed. Identified osteoblastic OS cells were divided into five subsets, and the subsets of those osteoblastic OS cells with significant prognostic correlation were determined using a deconvolution algorithm. Thereby, different transcription patterns in the cellular subtypes of osteoblastic OS cells were reported, and key transcription factors associated with survival prognosis were identified. Furthermore, the regulation of osteolysis by osteoblastic OS cells via receptor activator of nuclear factor kappa-B ligand was revealed. Furthermore, the role of osteoblastic OS cells in regulating angiogenesis through vascular endothelial growth factor-A was revealed. C3_TXNIP+ macrophages and C5_IFIT1+ macrophages were found to regulate regulatory T cells and participate in CD8+ T cell exhaustion, illustrating the possibility of immunotherapy that could target CD8+ T cells and macrophages. Our findings here show that the role of C1_osteoblastic OS cells in OS is to promote osteolysis and angiogenesis, and this is associated with survival prognosis. In addition, T cell depletion is an important feature of OS. More importantly, the present study provided a valuable resource for the in-depth study of the heterogeneity of the OS TME.

Altered composition of the gut microbiota has been observed in many neurodegenerative diseases. LanCL1 has been proven to protect neurons and reduce oxidative stress. The present study was designed to investigate alterations of the gut microbiota in LanCL1 knockout mice and to study the interactions between gut bacteria and the brain. Wild-type and LanCL1 knockout mice on a normal chow diet were evaluated at 4 and 8–9 weeks of age. 16s rRNA sequence and untargeted metabolomics analyses were performed to investigate changes in the gut microbiota and feces metabolites. Real-time polymerase chain reaction analysis, AB-PAS staining, and a TUNEL assay were performed to detect alterations in the gut and brain of knockout mice. The serum cytokines of 9-week-old knockout mice, which were detected by a multiplex cytokine assay, were significantly increased. In the central nervous system, there was no increase of antioxidant defense genes even though there was only low activity of glutathione S-transferase in the brain of 8-week-old knockout mice. Interestingly, the gut tight junctions, zonula occludens-1 and occludin, also displayed a downregulated expression level in 8-week-old knockout mice. On the contrary, the production of mucus increased in 8-week-old knockout mice. Moreover, the compositions of the gut microbiota and feces metabolites markedly changed in 8-week-old knockout mice but not in 4-week-old mice. Linear discriminant analysis and t-tests identified Akkermansia as a specific abundant bacteria in knockout mice. Quite a few feces metabolites that have protective effects on the brain were reduced in 8-week-old knockout mice. However, N-acetylsphingosine was the most significant downregulated feces metabolite, which may cause the postponement of neuronal apoptosis. To further investigate the effect of the gut microbiota, antibiotics treatment was given to both types of mice from 5 to 11 weeks of age. After treatment, a significant increase of oxidative damage in the brain of knockout mice was observed, which may have been alleviated by the gut microbiota before. In conclusion, alterations of the gut microbiota and feces metabolites alleviated oxidative damage to the brain of LanCL1 knockout mice, revealing that an endogenous feedback loop mechanism of the microbiota-gut-brain axis maintains systemic homeostasis.

When mosaicking adjacent nightlight images of a large area that lacks human activities, traditional registration methods have difficulty realizing the tie point registrations due to the lack of structural information. In order to address this issue, this study devises an easy-to-implement engineering solution that allows for the registration of sparse light areas with high efficiency while guaranteeing accuracy in non-sparse light areas. The proposed method first extracts the sparsely distributed light point positions through use of roundness detection and the centroid method. Then, geometric positioning forward and backward algorithms and the random consistency sampling detection algorithm (RANSAC) are used in order to achieve a rough registration of the nightlight images and the remaining tie points are expanded through the affine model. Through experimentation it was found that, compared with traditional registration methods, the proposed method is more reliable and has a wider distribution in sparse light areas. Finally, through the registration test of 275 scenes of nightlight images of China from Luojia-1, the coverage ratio of the tie points was increased from 59.3% from the traditional method to 95.3% in this study and the accuracy of the block adjustment was 0.63 pixels, which verifies the effectiveness of the method. The proposed method provides a basis for the registration, block adjustment, and mosaicking of nightlight images.

Abstract The study aimed to investigate the associations between maternal lifestyles and antenatal stress and anxiety. 1491 pregnant women were drawn from the Guangxi birth cohort study (GBCS). A base line questionnaire was used to collect demographic information and maternal lifestyles. The Pregnancy Stress Rating Scale (PSRS) and Self-Rating Anxiety Scale (SAS) were used to assess prenatal stress and anxiety, respectively. Regression analyses identified the relationship between maternal lifestyles and prenatal stress and anxiety: (1) Hours of phone use per day was positively correlated to prenatal stress and anxiety and increased with stress and anxiety levels (all P trend 0.05). Our results showed that adverse lifestyles increase the risk of antenatal stress and anxiety, a regular routine and a variety of enjoyable activities decreases the risk of prenatal stress and anxiety.

Abstract Background Limited evidences were reported about the risk of pre-pregnancy hair dye use or irregular menstruation with abnormal birth weight during pregnancy, and their joint effects were also unknown. The aim of our study was to explore whether the pre-pregnancy exposure of hair dye and irregular menstruation were associated with the risk of abnormal birth weight. Methods We conducted a nested case-control study from a prospective cohort of 6203 pregnant women. Low birth weight study included 315 mother-infant pairs (105 LBW cases and 210 matched controls), and macrosomia study included 381 mother-infant pairs (127 macrosomia cases and 254 matched controls). Meanwhile, lifestyle information including hair dying custom and menstrual history were collected by face-to-face questionnaires and birth outcomes were extracted from the medical records. The logistic regressions models were used to analyze the join effect of irregular menstruation and hair dye use. Results Pre-pregnancy hair dye use was associated with increased risk of LBW (adjusted OR = 1.71, 95% CI: 1.01–2.92, P = 0.048). Irregular menstruation had high risk of LBW (adjusted OR = 2.79, 95% CI: 1.53–5.09, P = 0.001) and macrosomia (adjusted OR = 1.93, 95% CI: 1.09–3.44, P = 0.023). Additionally, in the LBW study, women who used hair dye with pre-pregnancy BMI

In hyperspectral imaging (HSI), stripe noise is one of the most common noise types that adversely affects its application. Convolutional neural networks (CNNs) have contributed to state-of-the-art performance in HSI destriping given their powerful feature extraction and learning capabilities. However, it is difficult to obtain paired training samples for real data. Most CNN destriping methods construct a paired training dataset with simulated stripe noise for network training. However, when the stripe noise of real data is complex, destriping performance of the model is constrained. To solve this problem, this study proposes a real HSI stripe removal method using a toward real HSI stripe removal via direction constraint hierarchical feature cascade network (TRS-DCHC). TRS-DCHC uses the stripe noise extract subnetwork to extract stripe patterns from real stripe-containing HSI data and incorporates clean images to form paired training samples. The destriping subnetwork advantageously utilizes a wavelet transform to explicitly decompose stripe and stripe-free components. It also adopts multi-scale feature dense connections and feature fusion to enrich feature information and deeply mine the discriminate features of stripe and stripe-free components. Our experiments on both simulated and real data of various loads showed that TRS-DCHC features better performance in both simulated and real data compared with state-of-the-art method.

Summary: Androgenetic haploid embryonic stem cells (AG-haESCs) hold great promise for exploring gene functions and generating gene-edited semi-cloned (SC) mice. However, the high incidence of self-diploidization and low efficiency of SC mouse production are major obstacles preventing widespread use of these cells. Moreover, although SC mice generation could be greatly improved by knocking out the differentially methylated regions of two imprinted genes, 50% of the SC mice did not survive into adulthood. Here, we found that the genome-wide DNA methylation level in AG-haESCs is extremely low. Subsequently, downregulation of both de novo methyltransferase Dnmt3b and other methylation-related genes was determined to be responsible for DNA hypomethylation. We further demonstrated that ectopic expression of Dnmt3b in AG-haESCs could effectively improve DNA methylation level, and the high incidence of self-diploidization could be markedly rescued. More importantly, the developmental potential of SC embryos was improved, and most SC mice could survive into adulthood. : Ectopic expression of Dnmt3b could rescue DNA methylation level in repetitive sequences of hypomethylated AG-haESCs, suppress high incidence of self-diploidization, and promote developmental potential of SC embryos, and most SC mice could survive into adulthood. Keywords: androgenetic haploid embryonic stem cells, self-diploidization, semi-cloned mice, DNA methylation, Dnmt3b

Abstract Chronic prostatitis (CP) is a complex disease. Fragmentary evidence suggests that factors such as infection and autoimmunity might be associated with CP. To further elucidate potential risk factors, the current study utilized the Fangchenggang Area Male Health and Examination Survey (FAMHES) project; where 22 inflammatory/immune markers, hormone markers, tumor-related proteins, and nutrition-related variables were investigated. We also performed baseline, regression, discriminant, and receiver operating characteristic (ROC) analyses. According to NIH-Chronic Prostatitis Symptom Index (NIH-CPSI), participants were divided into chronic prostatitis/chronic pelvic pain syndrome (CP/CPPS, pain ≥ 4; divided into IIIa and IIIb sub-groups) and non-CPPS (pain = 0; divided into IV and normal sub-groups). Analyses revealed osteocalcin as a consistent protective factor for CP/CPPS, NIH-IIIb, and NIH-IV prostatitis. Further discriminant analysis revealed that ferritin (p = 0.002) and prostate-specific antigen (PSA) (p = 0.010) were significantly associated with NIH-IIIa and NIH-IV prostatitis, respectively. Moreover, ROC analysis suggested that ferritin was the most valuable independent predictor of NIH-IIIa prostatitis (AUC = 0.639, 95% CI = 0.534–0.745, p = 0.006). Together, our study revealed inflammatory/immune markers [immunoglobulin E, Complement (C3, C4), C-reactive protein, anti-streptolysin, and rheumatoid factors], hormone markers (osteocalcin, testosterone, follicle-stimulating hormone, and insulin), tumor-related proteins (carcinoembryonic and PSA), and a nutrition-related variable (ferritin) were significantly associated with CP or one of its subtypes.

Abstract The RTK/ERK signaling pathway has been implicated in prostate cancer progression. However, the genetic relevance of this pathway to aggressive prostate cancer at the SNP level remains undefined. Here we performed a SNP and gene-based association analysis of the RTK/ERK pathway with aggressive prostate cancer in a cohort comprising 956 aggressive and 347 non-aggressive cases. We identified several loci including rs3217869/CCND2 within the pathway shown to be significantly associated with aggressive prostate cancer. Our functional analysis revealed a statistically significant relationship between rs3217869 risk genotype and decreased CCND2 expression levels in a collection of 119 prostate cancer patient samples. Reduced expression of CCND2 promoted cell proliferation and its overexpression inhibited cell growth of prostate cancer. Strikingly, CCND2 downregulation was consistently observed in the advanced prostate cancer in 18 available clinical data sets with a total amount of 1,095 prostate samples. Furthermore, the lower expression levels of CCND2 markedly correlated with prostate tumor progression to high Gleason score and elevated PSA levels, and served as an independent predictor of biochemical relapse and overall survival in a large cohort of prostate cancer patients. Together, we have identified an association of genetic variants and genes in the RTK/ERK pathway with prostate cancer aggressiveness, and highlighted the potential importance of CCND2 in prostate cancer susceptibility and tumor progression to metastasis.

Mental fatigue has serious negative impacts on the brain cognitive functions and has been widely explored by the means of brain functional networks with the neuroimaging technique of electroencephalogram (EEG). Recently, several researchers reported that brain functional network constructed from EEG signals has fractal feature, raising an important question: what are the effects of mental fatigue on the fractal dimension of brain functional network? In the present study, the EEG data of alpha1 rhythm (8-10 Hz) at task state obtained by a mental fatigue model were chosen to construct brain functional networks. A modified greedy colouring algorithm was proposed for fractal dimension calculation in both binary and weighted brain functional networks. The results indicate that brain functional networks still maintain fractal structures even when the brain is at fatigue state; fractal dimension presented an increasing trend along with the deepening of mental fatigue fractal dimension of the weighted network was more sensitive to mental fatigue than that of binary network. Our current results suggested that mental fatigue has great regular impacts on the fractal dimension in both binary and weighted brain functional networks.

Urban ventilation corridors serve as channels of fresh air flow between the city and suburbs, helping to improve the wind and thermal environments and thermal comfort. However, owing to the limited number of weather stations, it is impossible to quantitatively reveal the effective effect range of urban ventilation corridors on urban thermal comfort at the scale of 100 × 100 m, which is optimal for urban ventilation corridors. In this study, we integrated building data, the European Centre for Medium-Range weather forecast data (ECMWF), MOD13Q1, and other multisource data to analyse the effect of urban ventilation corridors on urban thermal comfort at a unified scale of 100 × 100 m. The results showed that ECMWF and Landsat8 data could be used as substitute factors to improve the universal thermal climate index (UTCI) urban spatial resolution. The effective range of urban ventilation corridor effects on the urban surface temperature and urban comfort was ≤1000 m, with building density and vegetation coverage as the main factors limiting this range. Therefore, attention should be paid to the effective range of urban ventilation corridors, the surrounding building density, vegetation coverage, and the rational use of urban ventilation corridors to reduce the energy consumption of air conditioning in summer.

Brain functional network has been widely applied to investigate brain function changes among different conditions and proved to be a small-world-like network. But seldom researches explore the effects of mental fatigue on the small-world brain functional network organization. In the present study, 20 healthy individuals were included to do a consecutive mental arithmetic task to induce mental fatigue, and scalp electroencephalogram (EEG) signals were recorded before and after the task. Correlations between all pairs of EEG channels were determined by mutual information (MI). The resulting adjacency matrices were converted into brain functional networks by applying a threshold, and then, the clustering coefficient (C), characteristic path length (L), and corresponding small-world feature were calculated. Through performing analysis of variance (ANOVA) on the mean MI for every EEG rhythm, only the data of α1 rhythm during the task state were emerged for the further explorations of mental fatigue. For a wide range of thresholds, C increased and L and small-world feature decreased with the deepening mental fatigue. The pattern of the small-world characteristic still existed when computed with a constant degree. Our present findings indicated that more functional connectivities were activated at the mental fatigue stage for efficient information transmission and processing, and mental fatigue can be characterized by a reduced small-world network characteristic. Our results provide a new perspective to understand the neural mechanisms of mental fatigue based on complex network theories.

The maximum eigenvalue of the adjacency matrix (AM) has been supposed to contain rich information about the corresponding network. An experimental study focused on revealing the meaning and application of the maximum eigenvalue is missing. To this end, AM was constructed using mutual information (MI) to determine the functional connectivity with electroencephalogram (EEG) data recorded with a mental fatigue model, and then was converted into both binary and weighted brain functional network (BFN) and corresponding random networks (RNs). Both maximum eigenvalue and corresponding network characters in BFNs and RNs were considered to explore the changes during the formation of mental fatigue. The results indicated that large maximum eigenvalue means more edges in the corresponding network, along with a high degree and a short characteristic path length both in weighted and binary BFNs. Interestingly, the maximum eigenvalue of AM was always a little larger than that of the corresponding random matrix (RM), and had an obvious linearity with the sum of the AM elements, indicating that the maximum eigenvalue can be able to distinguish the network structures which have the same mean degree. What is more, the maximum eigenvalue, which increased with the deepening of mental fatigue, can become a good indicator for mental fatigue estimation.

The high-resolution nighttime light (NTL) data of the LuoJia1-01 NTL remote sensing satellite has enriched the available data of NTL remote sensing applications. The radiance calibration used as a reference to convert the digital number (DN) recorded by the nighttime sensor into the radiance of the corresponding ground object is the basic premise to the effective application of the NTL data. Owing to the lack of on-board calibration equipment and the absence of an absolute radiometric calibration light source at night, it is difficult for LuoJia1-01 to carry out on-orbit radiance calibration. The moon, as an exoatmospheric stable radiation source, is widely used for the radiometric calibration of remote sensing satellite sensors and to monitor the stability of the visible and near-infrared sensors. This study, based on lunar observation of the LuoJia1-01 NTL sensor, focused on on-orbit radiometric calibration and included monitoring changes in the nighttime sensor radiometric response for nearly a year by using the Robotic Lunar Observatory (ROLO) lunar irradiance model (Version 311 g). The results showed that: (1) the consistency of the radiometric calibration results based on the ROLO model and the laboratory calibration results of LuoJia1-01 exceeded 90%; (2) the nighttime sensor of LuoJia1-01 radiometric response underwent approximately 6% degradation during the observation period of nearly one year (353 days).

Ground control points (GCPs) are generally used to calibrate the installation between the camera and star sensor of a satellite in orbit and improve the geometric positioning accuracy of the satellite. However, the use of GCPs for high-frequency calibration is difficult, and it is particularly difficult to acquire accurate GCPs for the image of a nightlight satellite. In this study, we developed a camera-star sensor installation calibration method that eliminates the need for GCPs. In the proposed method, the camera and star sensor lenses are simultaneously pointed at the star, and the camera-star sensor installation is accurately calibrated by processing the star map obtained by the camera and star sensors. Reference data such as road network and Moon position data were used to verify the proposed method and evaluate its positioning accuracy. The results of the application of the method to the positioning of the Luojia 1-01 satellite indicated an accuracy within 800 m, which is comparable with that of the traditional method.