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The ovary is an important reproductive and endocrine organ for the continuation of the species and the homeostasis of the body's internal environment. Arsenic exposure is a global public health problem. However, the damage to the ovaries caused by exposure to arsenic-contaminated drinking water from neonatal mice period remains unclear. Here, we showed that arsenic exposure resulted in reduced granulosa cell proliferation, diminished ovarian reserve, decreased oogenesis, and endocrine disruption in mice. Mechanistically, arsenic exposure decreased the protein level of METTL3 in granulosa cells. The m6A modification levels of mitophagy regulated gene BNIP3 in 3’UTR region was decreased in arsenic exposed granulosa cells. Meanwhile, YTHDF2, which decays mRNA, bound to the 3′UTR region of BNIP3 was also decreased in arsenic exposed ovarian granulosa cells. Thus, BNIP3 mRNA becames more stable, and mitophagy was increased. The excessive mitophagy in granulosa cells led to endocrine disruption, follicular atresia and diminished ovarian reserve. In summary, our study reveals that METTL3-dependent m6A modification regulates granulosa cell mitophagy and follicular atresia by targeting BNIP3 which are induced by arsenic exposure.

Semantic segmentation of remote sensing images plays a critical role in areas such as urban change detection, environmental protection, and geohazard identification. Convolutional Neural Networks (CNNs) have been excessively employed for semantic segmentation over the past few years; however, a limitation of the CNN is that there exists a challenge in extracting the global context of remote sensing images, which is vital for semantic segmentation, due to the locality of the convolution operation. It is informed that the recently developed Transformer is equipped with powerful global modeling capabilities. A network called TCNet is proposed in this article, and a parallel-in-branch architecture of the Transformer and the CNN is adopted in the TCNet. As such, the TCNet takes advantage of both Transformer and CNN, and both global context and low-level spatial details could be captured in a much shallower manner. In addition, a novel fusion technique called Interactive Self-attention is advanced to fuse the multilevel features extracted from both branches. To bridge the semantic gap between regions, a skip connection module called Windowed Self-attention Gating is further developed and added to the progressive upsampling network. Experiments on three public datasets (i.e., Bijie Landslide Dataset, WHU Building Dataset, and Massachusetts Buildings Dataset) depict that TCNet yields superior performance over state-of-the-art models. The IoU values obtained by TCNet for these three datasets are 75.34% (ranked first among 10 models compared), 91.16% (ranked first among 13 models compared), and 76.21% (ranked first among 13 models compared), respectively.

The ground deformation rate is an important index for evaluating the stability and degradation of permafrost. Due to limited accessibility, in-situ measurement of the ground deformation of permafrost areas on the Tibetan Plateau is a challenge. Thus, the technique of time-series interferometric synthetic aperture radar (InSAR) is often adopted for measuring the ground deformation rate of the permafrost area, the effectiveness of which is, however, degraded in areas with geometric distortions in synthetic aperture radar (SAR) images. In this study, a method that integrates InSAR and the random forest method is proposed for an improved permafrost stability mapping on the Tibetan Plateau; to demonstrate the application of the proposed method, the permafrost stability mapping in a small area located in the central region of the Tibetan Plateau is studied. First, the ground deformation rate in the concerned area is studied with InSAR, in which 67 Sentinel-1 scenes taken in the period from 2014 to 2020 are collected and analyzed. Second, the relationship between the environmental factors (i.e., topography, land cover, land surface temperature, and distance to road) and the permafrost stability is mapped with the random forest method based on the high-quality data extracted from the initial InSAR analysis. Third, the permafrost stability in the whole study area is mapped with the trained random forest model, and the issue of data scarcity in areas where the terrain visibility of SAR images is poor or InSAR results are not available in permafrost stability mapping can be overcome. Comparative analyses demonstrate that the integration of the InSAR and the random forest method yields a more effective permafrost stability mapping compared with the sole application of InSAR analysis.

The impact of blast wave air overpressure (AOp) may cause damage to nearby structures and significant noise pollution. In order to minimize the impact of the blast wave on the buildings around the tunnel, a new wave-blocking trolley (NWBT) was proposed to control the AOp during the construction of the Baitacun tunnel, as the nearby villages were located less than 100 m from the tunnel portal. In the study, the characteristics of the blast wave pressure profiles were studied by field measurements and numerical simulations and the Friedlander equation parameters were obtained. Second, the controlling effect and working mechanism of the NWBT were verified to be effective. The measured peak AOp was weakened from 2.49 kPa to 0.55 kPa with the operating NWBT, which meets the requirements of Chinese specifications. Furthermore, a numerical simulation for the impact process of the NWBT was established using the ANSYS/LS-DYNA software. Finally, it was found that a shorter distance from the NWBT to the source of the blast makes a better contribution to its wave-blocking effect. The waveform characteristics obtained in the study contribute to the design of tunnel surrounding structures; the application of the NWBT is a successful wave-blocking method for tunnels adjacent to environmentally sensitive areas.

Frequent epileptic seizures cause damage to the human brain, resulting in memory impairment, mental decline, and so on. Therefore, it is important to detect epileptic seizures and provide medical treatment in a timely manner. Currently, medical experts recognize epileptic seizure activity through the visual inspection of electroencephalographic (EEG) signal recordings of patients based on their experience, which takes much time and effort. In view of this, this paper proposes a one-dimensional convolutional neural network-long short-term memory (1D CNN-LSTM) model for automatic recognition of epileptic seizures through EEG signal analysis. Firstly, the raw EEG signal data are pre-processed and normalized. Then, a 1D convolutional neural network (CNN) is designed to effectively extract the features of the normalized EEG sequence data. In addition, the extracted features are then processed by the LSTM layers in order to further extract the temporal features. After that, the output features are fed into several fully connected layers for final epileptic seizure recognition. The performance of the proposed 1D CNN-LSTM model is verified on the public UCI epileptic seizure recognition data set. Experiments results show that the proposed method achieves high recognition accuracies of 99.39% and 82.00% on the binary and five-class epileptic seizure recognition tasks, respectively. Comparing results with traditional machine learning methods including k-nearest neighbors, support vector machines, and decision trees, other deep learning methods including standard deep neural network and CNN further verify the superiority of the proposed method.

Epilepsy is a prevalent neurological disorder that threatens human health in the world. The most commonly used method to detect epilepsy is using the electroencephalogram (EEG). However, epilepsy detection from the EEG is time-consuming and error-prone work because of the varying levels of experience we find in physicians. To tackle this challenge, in this paper, we propose a multi-scale non-local (MNL) network to achieve automatic EEG signal detection. Our MNL-Network is based on 1D convolution neural network involving two specific layers to improve the classification performance. One layer is named the signal pooling layer which incorporates three different sizes of 1D max-pooling layers to learn the multi-scale features from the EEG signal. The other one is called a multi-scale non-local layer, which calculates the correlation of different multi-scale extracted features and outputs the correlative encoded features to further enhance the classification performance. To evaluate the effectiveness of our model, we conduct experiments on the Bonn dataset. The experimental results demonstrate that our MNL-Network could achieve competitive results in the EEG classification task.

The technique of interferometric synthetic aperture radar (InSAR) is increasingly employed for landslide detection over large areas, even though the limitations of initial InSAR analysis results have been well acknowledged. Steep terrain in mountainous areas may cause geometric distortions of SAR images, which could affect the accuracy of InSAR analysis results. In addition, due to the existence of massive ground deformation points in the initial InSAR analysis results, accurate landslide recognition from the initial results is challenging. To efficiently identify potential landslide areas from the ascending–descending SAR datasets, this paper presents a novel interpretation approach to analyze the initial time-series InSAR analysis results. Within the context of the proposed approach, SAR visibility analysis, conversion analysis of deformation rates obtained from the time-series InSAR analysis, and spatial analysis and statistics tools for cluster extraction are incorporated. The effectiveness of the proposed approach is illustrated through a case study of landslide identification in Danba, a county in Sichuan, China. The potential landslide regions in the study area are identified based on the interpretation of small baseline subset InSAR (SBAS-InSAR) results, obtained with ascending–descending Sentinel-1A datasets. Finally, on the basis of the field survey results, a total of 21 landslides are detected in the potential landslide regions identified, through which the results obtained from the proposed interpretation approach are tested.

The interferometric synthetic aperture radar (InSAR) technique is widely adopted for detecting and monitoring landslides, but its effectiveness is often degraded in mountainous terrains, due to geometric distortions in the synthetic aperture radar (SAR) image input. To evaluate the terrain effect on the applicability of InSAR in landslide monitoring, a variety of visibility evaluation models have been developed, among which the R-index models are quite popular. In consideration of the poor performance of the existing R-index models in the passive layover region, this study presents an improved R-index model, in which a coefficient for improving the visibility evaluation in the far passive layover regions is incorporated. To demonstrate the applicability of the improved R-index model, the terrain visibility of SAR images in Fengjie, a county in the Three Gorges Reservoirs region, China, is studied. The effectiveness of the improved R-index model is demonstrated through comparing the visibility evaluation results with those obtained from the existing R-index models and P-NG method. Further, the effects of the line-of-sight (LOS) parameters of SAR images and the resolution of the digital elevation model (DEM) on the terrain visibility are discussed.

The follicle is the basic structural and functional unit of the ovary in female mammals. The excessive depletion of follicles will lead to diminished ovarian reserve or even premature ovarian failure, resulting in diminished ovarian oogenesis and endocrine function. Excessive follicular depletion is mainly due to loss of primordial follicles. Our analysis of published human ovarian single-cell sequencing results by others revealed a significant increase in rho-associated protein kinase 1 (ROCK1) expression during primordial follicle development. However, the role of ROCK1 in primordial follicle development and maintenance is not clear. This study revealed a gradual increase in ROCK1 expression during primordial follicle activation. Inhibition of ROCK1 resulted in reduced primordial follicle activation, decreased follicular reserve, and delayed development of growing follicles. This effect may be achieved through the HIPPO pathway. The present study indicates that ROCK1 is a key molecule for primordial follicular reserve and follicular development. NEW & NOTEWORTHY: ROCK1, one of the Rho GTPases, plays an important role in primordial follicle reserve and follicular development. ROCK1 was primarily expressed in the cytoplasm of oocytes and granulosa cell in mice. Inhibition of ROCK1 significantly reduced the primordial follicle reserve and delayed growing follicle development. ROCK1 regulates primordial follicular reserve and follicle development through the HIPPO signaling pathway. These findings shed new lights on the physiology of sustaining female reproduction. [ABSTRACT FROM AUTHOR]