Characterization of sexual maturity-associated N6-methyladenosine in boar testes.

BACKGROUND: The health and size of the testes are crucial for boar fertility. Testicular development is tightly regulated by epigenetics. N6-methyladenosine (m6A) modification is a prevalent internal modification on mRNA and plays an important role in development. The mRNA m6A methylation in boar testicular development still needs to be investigated. RESULTS: Using the MeRIP-seq technique, we identify and profile m6A modification in boar testes between piglets and adults. The results showed 7783 distinct m6A peaks in piglets and 6590 distinct m6A peaks in adults, with 2,471 peaks shared between the two groups. Enrichment of GO and KEGG analysis reveal dynamic m6A methylation in various biological processes and signalling pathways. Meanwhile, we conjointly analyzed differentially methylated and expressed genes in boar testes before and after sexual maturity, and reproductive related genes (TLE4, TSSK3, TSSK6, C11ORF94, PATZ1, PHLPP1 and PAQR7) were identified. Functional enrichment analysis showed that differential genes are associated with important biological functions, including regulation of growth and development, regulation of metabolic processes and protein catabolic processes. CONCLUSION: The results demonstrate that m6A methylation, differential expression and the related signalling pathways are crucial for boar testicular development. These results suggest a role for m6A modification in boar testicular development and provided a resource for future studies on m6A function in boar testicular development.

Single-shot lensless masked imaging with enhanced self-calibrated phase retrieval.

Single-shot lensless imaging with a binary amplitude mask enables a low-cost and miniaturized configuration for wave field recovery. However, the mask only allows a part of the wave field to be captured, and thus the inverse decoding process becomes a highly ill-posed problem. Here we propose an enhanced self-calibrated phase retrieval (eSCPR) method to realize single-shot joint recovery of mask distribution and the sample's wavefront. In our method, a sparse regularized phase retrieval (SrPR) algorithm is designed to calibrate the mask distribution. Then, a denoising regularized phase retrieval (DrPR) algorithm is constructed to reconstruct the wavefront of the sample. Compared to conventional single-shot methods, our method shows robust and flexible image recovery. Experimental results of different samples are given to demonstrate the superiority of our method.

Dual-constrained physics-enhanced untrained neural network for lensless imaging.

An untrained neural network (UNN) paves a new way to realize lensless imaging from single-frame intensity data. Based on the physics engine, such methods utilize the smoothness property of a convolutional kernel and provide an iterative self-supervised learning framework to release the needs of an end-to-end training scheme with a large dataset. However, the intrinsic overfitting problem of UNN is a challenging issue for stable and robust reconstruction. To address it, we model the phase retrieval problem into a dual-constrained untrained network, in which a phase-amplitude alternating optimization framework is designed to split the intensity-to-phase problem into two tasks: phase and amplitude optimization. In the process of phase optimization, we combine a deep image prior with a total variation prior to retrain the loss function for the phase update. In the process of amplitude optimization, a total variation denoising-based Wirtinger gradient descent method is constructed to form an amplitude constraint. Alternative iterations of the two tasks result in high-performance wavefield reconstruction. Experimental results demonstrate the superiority of our method.

Lensless masked imaging with self-calibrated phase retrieval.

Lensless imaging with a mask is an attractive topic as it enables a compact configuration to acquire wavefront information of a sample with computational approaches. Most existing methods choose a customized phase mask for wavefront modulation and then decode the sample's wave field from modulated diffraction patterns. Different from phase masks, lensless imaging with a binary amplitude mask facilitates a cheaper fabrication cost, but high-quality mask calibration and image reconstruction have not been well resolved. Here we propose a self-calibrated phase retrieval (SCPR) method to realize a joint recovery of a binary mask and sample's wave field for a lensless masked imaging system. Compared with conventional methods, our method shows a high-performance and flexible image recovery without the help of an extra calibration device. Experimental results of different samples demonstrate the superiority of our method.

Lensfree on-chip microscopy based on single-plane phase retrieval.

We propose a novel single-plane phase retrieval method to realize high-quality sample reconstruction for lensfree on-chip microscopy. In our method, complex wavefield reconstruction is modeled as a quadratic minimization problem, where total variation and joint denoising regularization are designed to keep a balance of artifact removal and resolution enhancement. In experiment, we built a 3D-printed field-portable platform to validate the imaging performance of our method, where resolution chart, dynamic target, transparent cell, polystyrene beads, and stained tissue sections are employed for the imaging test. Compared to state-of-the-art methods, our method eliminates image degradation and obtains a higher imaging resolution. Different from multi-wavelength or multi-height phase retrieval methods, our method only utilizes a single-frame intensity data record to accomplish high-fidelity reconstruction of different samples, which contributes a simple, robust, and data-efficient solution to design a resource-limited lensfree on-chip microscope. We believe that it will become a useful tool for telemedicine and point-of-care application.

Image scanning microscopy with a long depth of focus generated by an annular radially polarized beam.

Image scanning microscopy (ISM) is a promising tool for bioimaging owing to its integration of signal to noise ratio (SNR) and super resolution superior to that obtained in confocal scanning microscopy. In this paper, we introduce the annular radially polarized beam to the ISM, which yields an axially extended excitation focus and enhanced resolution, providing a new possibility to obtain the whole information of thick specimen with a single scan. We present the basic principle and a rigorous theoretical model for ISM with annular radially polarized beam (ISM-aRP). Results show that the resolution of ISM-aRP can be enhanced by 4% compared with that in conventional ISM, and the axial extent of the focus is longer than 6λ. The projected view of the simulated fluorescent beads suspension specimen demonstrates the validity of ISM-aRP to obtain the whole information of volume sample. Moreover, this simple method can be easily integrated into the commercial laser scanning microscopy systems.