: What are the risks associated with cryopreserved semen collected during and after the coronavirus disease 2019 (COVID-19) pandemic wave in Wuhan, China? : We conducted a retrospective cohort study involving young adult men who were qualified sperm donors at the Hunan Province Human Sperm Bank (China) during the pandemic wave (January 1, 2020, to January 30, 2020) and after the wave and return to work (April 7, 2020, to May 30, 2020). One hundred paired semen and blood specimens from 100 donors were included, 50 paired semen and blood samples collected during the pandemic from 50 donors, and 50 semen and blood samples from another 50 donors collected upon work resumption when COVID-19 infections in the population were very low. Considering the low viral load in both the specimens, we used one-step single-tube nested quantitative real-time PCR (OSN-qRT-PCR) for SARS-CoV-2 detection. Moreover, to control the unacceptable risk of false-negative results, a second round of screening was performed with pooled RNA from negative semen samples using crystal digital PCR (cd-PCR). Naso-oropharyngeal swabs were not evaluated during the pandemic peak, but were negative for the 50 donors who collected semen after the COVID-19 wave of infections. : For individual blood and semen samples, the target genes, namely nucleocapsid protein (N) and open reading frame (ORF-1ab) genes, tested negative in all of the 100 paired samples. Further, as per cd-PCR results, there were >20,000 droplets per well in the RNA for each combined sample and no positive droplets were present for either of the aforementioned target genes. A total of 100 paired semen and blood samples from these two groups tested negative for SARS-CoV-2. : Cryopreserved semen at the Hunan Province Human Sperm Bank during and after the coronavirus disease 2019 (COVID-19) pandemic wave was free of SARS-CoV-2, and was judged safe for external use in the future.Utmost precaution should be exercised by all human sperm banks, and the use of highly secure devices and segregated cryovessels is encouraged.

Radargrammetry is a widely used methodology to generate the large-scale Digital Surface Model (DSM). Stereo matching is the most challenging step in radargrammetry due to the significant geometric differences and the inherent speckle noise. The speckle noise results in significant grayscale differences of the same feature points, which makes the traditional Horn–Schunck (HS) flow or multi-window zero-mean normalized cross-correlation (ZNCC) methods degrade. Therefore, this paper proposes an algorithm named Epipolar HS-NCC Flow (EHNF) for dense stereo matching, which is an improved HS flow method with normalized cross-correction constraint based on epipolar stereo images. First, the epipolar geometry is applied to resample the image to realize the coarse stereo matching. Subsequently, the EHNF method forms a global energy function to achieve fine stereo matching. The EHNF method constructs a local normalized cross-correlation constraint term to compensate for the grayscale invariance constraint, especially for the SAR stereo images. Additionally, two assessment methods are proposed to calculate the optimal cross-correlation parameter and smoothness parameter according to the refined matched point pairs. Two GaoFen-3 (GF-3) image pairs from ascending and descending orbits and the open Light Detection and Ranging (LiDAR) data are utilized to fully evaluate the proposed method. The results demonstrate that the EHNF algorithm improves the DSM elevation accuracy by 9.6% and 27.0% compared with the HS flow and multi-window ZNCC methods, respectively.

Human spermatogonial stem cells (SSCs) are the basis of spermatogenesis. However, little is known about the developmental regulatory mechanisms of SSC due to sample origin and species differences. To investigates the mechanisms involved in the proliferation of human SSC. The expression of mitogen-activated protein kinase kinase 7 (MKK7) in human testis was identified using immunohistochemistry and western blotting (WB). MKK7 was knocked down using small interfering RNA, and cell proliferation and apoptosis were detected by WB, EdU, cell counting kit-8 and fluorescence-activated cell sorting. After bioinformatic analysis, the interaction of MKK7 with c-Jun N-terminal kinases ( JNKs ) was verified by protein co-immunoprecipitation and WB. The phosphorylation of JNKs was inhibited by SP600125, and the phenotypic changes were detected by WB, cell counting kit-8 and fluorescence-activated cell sorting. MKK7 is mainly expressed in human SSCs, and MKK7 knockdown inhibits SSC proliferation and promotes their apoptosis. MKK7 mediated the phosphorylation of JNKs, and after inhibiting the phosphorylation of JNKs, the phenotypic changes of the cells were similar to those after MKK7 downregulation. The expression of MKK7 was significantly downregulated in patients with abnormal spermatogenesis, suggesting that abnormal MKK7 may be associated with spermatogenesis impairment. MKK7 regulates the proliferation and apoptosis of human SSC by mediating the phosphorylation of JNKs.

In this paper, we introduce a refined three-stage inversion algorithm (TSIA) for forest height estimation using polarimetric interferometric synthetic aperture radar (PolInSAR). Specifically, the iterative extraction of the boundary of the coherence region (IEBCR) and iterative look-up table (ILUT) are proposed to improve the efficiency of traditional TSIA. A class of refined TSIA utilizes the boundary of the coherence region (BCR) to alleviate the underestimation phenomenon in forest height estimation. Given many eigendecompositions in the extraction of BCR (EBCR), we analyze the relationship of eigenvectors between the adjacent points on the BCR and propose the IEBCR utilizing the power methods. In the final inversion stage of TSIA, the look-up table (LUT) uses the exhaustive search method to minimize the loss function in the 2-D grid with defined step sizes and thus costs high computational complexity. To alleviate the deficiency, we define the random volume over ground (RVoG) function based on the RVoG model and prove its monotonicity and convergence from the analytical and numerical points of view. After analyzing the relationship between the RVoG function and the loss function, we propose the ILUT for the inversion stage. The simulation and experiments based on the BioSAR 2008 campaign data illustrate that the IEBCR and ILUT greatly improve the computational efficiency almost without compromising on accuracy.

This paper presents a method for extracting the digital elevation model (DEM) of forested areas from polarimetric interferometric synthetic aperture radar (PolInSAR) data. The method models the ground phase as a Von Mises distribution, with a mean of the topographic phase computed from an external DEM. By combining the prior distribution of the ground phase with the complex Wishart distribution of the observation covariance matrix, we derive the maximum a posterior (MAP) inversion method based on the RVoG model and analyze its Cramer–Rao Lower Bound (CRLB). Furthermore, considering the characteristics of the objective function, this paper introduces a Four-Step Optimization (FSO) method based on gradient optimization, which solves the inefficiency problem caused by exhaustive search in solving ground phase using the MAP method. The method is validated using spaceborne L-band repeat-pass SAOCOM data from a test forest area. The test results for FSO indicate that it is approximately 5.6 times faster than traditional methods without compromising accuracy. Simultaneously, the experimental results demonstrate that the method effectively solves the problem of elevation jumps in DEM inversion when modeling the ground phase with the Gaussian distribution. ICESAT-2 data are used to evaluate the accuracy of the inverted DEM, revealing that our method improves the root mean square error (RMSE) by about 23.6% compared to the traditional methods.

Existing forest height estimation methods based on polarimetric interferometric synthetic aperture radar (PolInSAR) typically process each pixel independently, potentially introducing inconsistent estimates and additional decorrelation in the covariance matrix estimation. To address these limitations and effectively exploit the spatial context information, this paper proposes the first patch-based inversion method named joint pixel optimization inversion (JPO). By leveraging the smoothness and regularity of homogeneous pixels, a joint-pixel optimization problem is constructed, incorporating a first-order regularization on the ground phase. To solve the non-parallelizable problem of the alternating direction method of multipliers (ADMM), we devise a new parallelizable ADMM algorithm and prove its sublinear convergence. With the contextual information of neighboring pixels, JPO can provide more reliable forest height estimation and reduce the overestimation caused by additional decorrelation. The effectiveness of the proposed method is verified using spaceborne L-band repeat-pass SAOCOM acquisitions and LiDAR heights obtained from ICESat-2. Quantitative evaluations in forest height estimation show that the proposed method achieves a lower mean error (1.23 m) and RMSE (3.67 m) than the existing method (mean error: 3.09 m; RMSE: 4.70 m), demonstrating its improved reliability.