Phytochrome-interacting factors orchestrate hypocotyl adventitious root initiation in Arabidopsis.

Adventitious roots (ARs) are an important type of plant root and display high phenotypic plasticity in response to different environmental stimuli. It is known that photoreceptors inhibit darkness-induced hypocotyl adventitious root (HAR) formation by directly stabilizing Aux/IAA proteins. In this study, we further report that phytochrome-interacting factors (PIFs) plays a central role in HAR initiation by simultaneously inducing the expression of genes involved in auxin biosynthesis, auxin transport and the transcriptional control of root primordium initiation. We found that, on the basis of their activity downstream of phytochrome, PIFs are required for darkness-induced HAR formation. Specifically, PIFs directly bind to the promoters of some genes involved in root formation, including auxin biosynthesis genes YUCCA2 (YUC2) and YUC6, the auxin influx carrier genes AUX1 and LAX3, and the transcription factors WOX5/7 and LBD16/29, to activate their expression. These findings reveal a previously uncharacterized transcriptional regulatory network underlying HAR formation.

A genome-wide association study identifies a transporter for zinc uploading to maize kernels.

The Zn content in cereal seeds is an important trait for crop production as well as for human health. However, little is known about how Zn is loaded to plant seeds. Here, through a genome-wide association study (GWAS), we identify the Zn-NA (nicotianamine) transporter gene ZmYSL2 that is responsible for loading Zn to maize kernels. High promoter sequence variation in ZmYSL2 most likely drives the natural variation in Zn concentrations in maize kernels. ZmYSL2 is specifically localized on the plasma membrane facing the maternal tissue of the basal endosperm transfer cell layer (BETL) and functions in loading Zn-NA into the BETL. Overexpression of ZmYSL2 increases the Zn concentration in the kernels by 31.6%, which achieves the goal of Zn biofortification of maize. These findings resolve the mystery underlying the loading of Zn into plant seeds, providing an efficient strategy for breeding or engineering maize varieties with enriched Zn nutrition.

Thin and lightweight camera based on Pancake lens and deep learning.

Computational imaging using a Pancake lens can help reduce the size of optical systems by folded optics. However, Pancake cameras frequently exhibit inferior image quality due to stray light, low light transmission, and spatially varying aberrations. In this Letter, we propose a thin and lightweight camera comprising a polarization-based catadioptric Pancake lens and a Fourier Position encoding Network (FPNet). The camera achieves high-quality imaging at an f-number of 0.4 and an expansive 88° field of view. The FPNet encodes the positional order of the point spread functions, mitigating global optical image degradation and improving image quality by 10.13 dB in PSNR. The Pancake camera and FPNet have potential applications in mobile photography and virtual/augmented reality.

Autofocusing of Fresnel zone aperture lensless imaging for QR code recognition.

Fresnel zone aperture (FZA) lensless imaging encodes the incident light into a hologram-like pattern, so that the scene image can be numerically focused at a long imaging range by the back propagation method. However, the target distance is uncertain. The inaccurate distance causes blurs and artifacts in the reconstructed images. This brings difficulties for the target recognition applications, such as quick response code scanning. We propose an autofocusing method for FZA lensless imaging. By incorporating the image sharpness metrics into the back propagation reconstruction process, the method can acquire the desired focusing distance and reconstruct noise-free high-contrast images. By combining the Tamura of the gradient metrics and nuclear norm of gradient, the relative error of estimated object distance is only 0.95% in the experiment. The proposed reconstruction method significantly improves the mean recognition rate of QR code from 4.06% to 90.00%. It paves the way for designing intelligent integrated sensors.

Toward a see-through camera via AR lightguide.

As the foundation of virtual content generation, cameras are crucial for augmented reality (AR) applications, yet their integration with transparent displays has remained a challenge. Prior efforts to develop see-through cameras have struggled to achieve high resolution and seamless integration with AR displays. In this work, we present LightguideCam, a compact and flexible see-through camera based on an AR lightguide. To address the overlapping artifacts in measurement, we present a compressive sensing algorithm based on an equivalent imaging model that minimizes computational consumption and calibration complexity. We validate our design using a commercial AR lightguide and demonstrate a field of view of 23.1° and an angular resolution of 0.1° in the prototype. Our LightguideCam has great potential as a plug-and-play extensional imaging component in AR head-mounted displays, with promising applications for eye-gaze tracking, eye-position perspective photography, and improved human-computer interaction devices, such as full-screen mobile phones.

Explicit-restriction convolutional framework for lensless imaging.

Mask-based lensless cameras break the constraints of traditional lens-based cameras, introducing highly flexible imaging systems. However, the inherent restrictions of imaging devices lead to low reconstruction quality. To overcome this challenge, we propose an explicit-restriction convolutional framework for lensless imaging, whose forward model effectively incorporates multiple restrictions by introducing the linear and noise-like nonlinear terms. As examples, numerical and experimental reconstructions based on the limitation of sensor size, pixel pitch, and bit depth are analyzed. By tailoring our framework for specific factors, better perceptual image quality or reconstructions with 4× pixel density can be achieved. This proposed framework can be extended to lensless imaging systems with different masks or structures.

High-speed computer-generated holography using an autoencoder-based deep neural network.

Learning-based computer-generated holography (CGH) provides a rapid hologram generation approach for holographic displays. Supervised training requires a large-scale dataset with target images and corresponding holograms. We propose an autoencoder-based neural network (holoencoder) for phase-only hologram generation. Physical diffraction propagation was incorporated into the autoencoder's decoding part. The holoencoder can automatically learn the latent encodings of phase-only holograms in an unsupervised manner. The proposed holoencoder was able to generate high-fidelity 4K resolution holograms in 0.15 s. The reconstruction results validate the good generalizability of the holoencoder, and the experiments show fewer speckles in the reconstructed image compared with the existing CGH algorithms.

DNN-FZA camera: a deep learning approach toward broadband FZA lensless imaging.

In mask-based lensless imaging, iterative reconstruction methods based on the geometric optics model produce artifacts and are computationally expensive. We present a prototype of a lensless camera that uses a deep neural network (DNN) to realize rapid reconstruction for Fresnel zone aperture (FZA) imaging. A deep back-projection network (DBPN) is connected behind a U-Net providing an error feedback mechanism, which realizes the self-correction of features to recover the image detail. A diffraction model generates the training data under conditions of broadband incoherent imaging. In the reconstructed results, blur caused by diffraction is shown to have been ameliorated, while the computing time is 2 orders of magnitude faster than the traditional iterative image reconstruction algorithms. This strategy could drastically reduce the design and assembly costs of cameras, paving the way for integration of portable sensors and systems.

Investigating the Aging Effects of Biochar on Soil C and Si Dissolution and the Interactive Impact on Copper Immobilization.

Aging tests were used to investigate the long-term effects of BC on the immobilization of Cu, and the soil silicon dissolution of three types soils (black soil, (BS), vegetable garden soil (VS) and red soil (RS)). Litchi branch biochars (BC) at 10% (w/w) were incubated with three Cu (400 mg/kg) contaminated soils. The effect on soil properties of pH, soil organic carbon (SOC), dissolved organic carbon (DOC) and available silicon content were investigated, along with the speciation distribution of Cu. The results indicated that SOC, DOC, and available silicon content (except, BC300) increased with the application of BCs. On the other hand, the DTPA (diethylenetriaminepentaacetic acid) extractable Cu content in BS, VS and RS soils were reduced by 4-12%, 18-25%, and 12-19%, respectively. The Cu availability in all soils first increased, and then decreased during the aging process. The sum of the other four fractions, including the carbonate fraction and the inert component increased by 4-4.5% (BS), 1.4-2.1% (VS), and 0.5-1% (RS) respectively, over the long-term process. Moreover, during the whole aging process, the soil properties (such as pH, SOC, DOC and available silicon content) were almost stable. This study demonstrates that BCs, especially those produced at a higher temperature, are superior to those been produced at 300 °C in immobilizing Cu and releasing available silicon in soils. However, the remediation efficiencies were restricted by the soil type contamination status and remediation time.

Assessing biochar application to immobilize Cd and Pb in a contaminated soil: a field experiment under a cucumber-sweet potato-rape rotation.

Cd and Pb are common toxic contaminants prevailing in agricultural soils contaminated by mining activities. In this study, biochar was used to stabilize Cd and Pb contaminated soil for safe with three crops rotation condition within one year. Field experiments were carried out to investigate the effect of Litchi branch biochar (BC), pyrolyzed at 600 °C and applied at 4 rates [(0 t ha-1 (T0), 10 t ha-1 (T1), 20 t ha-1 (T2) and 30 t ha-1 (T3)]) on remediation of Cd and Pb in agricultural soil near Dabaoshan Mine in South China under a cucumber-sweet potato-rape rotation. The results showed that the application of BC can significantly increase the pH, cation exchange capacity and soil organic matter. After cultivation of crops, the pH values decrease gradually, with the biggest drop of 0.45 pH units in T3 treatment after rape cultivation. BC application increased the yield of three crops up to onefold to twofold in T3 treatment as compared to the control. The uptake of Cd and Pb in all three crops decreases with the increase in BC doses, which is mainly related to the decrease in bioavailable metals in their respective soil treatments. Under 1-year crops rotation, the remediation ability of BC still remains, while Cd and Pb can exhibit different risk to different crops. The data of this study can provide scientific suggestions for the selection of suitable crops and proper BC amount in remediation of heavy metal contaminated soil.

Structured light field 3D imaging.

In this paper, we propose a method by means of light field imaging under structured illumination to deal with high dynamic range 3D imaging. Fringe patterns are projected onto a scene and modulated by the scene depth then a structured light field is detected using light field recording devices. The structured light field contains information about ray direction and phase-encoded depth, via which the scene depth can be estimated from different directions. The multidirectional depth estimation can achieve high dynamic 3D imaging effectively. We analyzed and derived the phase-depth mapping in the structured light field and then proposed a flexible ray-based calibration approach to determine the independent mapping coefficients for each ray. Experimental results demonstrated the validity of the proposed method to perform high-quality 3D imaging for highly and lowly reflective surfaces.

Optical cryptosystem based on phase-truncated Fresnel diffraction and transport of intensity equation.

A novel optical cryptosystem based on phase-truncated Fresnel diffraction (PTFD) and transport of intensity equation (TIE) is proposed. By using the phase truncation technique, a phase-encoded plaintext could be encrypted into a real-valued noise-like intensity distribution by employing a random amplitude mask (RAM) and a random phase mask (RPM), which are regarded as two secret keys. For decryption, a generalized amplitude-phase retrieval (GAPR) algorithm combined with the TIE method are proposed to recover the plaintext with the help of two keys. Different from the current phase-truncated-based optical cryptosystems which need record the truncated phase as decryption keys, our scheme do not need the truncated phase because of the introducing of the TIE method. Moreover, the proposed scheme is expected to against existing attacks. A set of numerical simulation results show the feasibility and security of the proposed method.

Vulnerability to ciphertext-only attack of optical encryption scheme based on double random phase encoding.

We demonstrate in this paper that the traditional double random phase encoding (DRPE) technique is vulnerable to ciphertext-only attack (COA). In this method, an unauthorized user (or say attacker) is assumed to be able to retrieve the corresponding plaintext from the only ciphertext under some certain condition. The proposed scheme mainly relies on a hybrid iterative phase retrieval (HIPR) algorithm, which combines various phase retrieval algorithms. With an estimation of the number of nonzero pixels (NNP) in the original plaintext, an attacker could recover the plaintext in a large extent. The simulation results show that this method is feasible and validate.

Causal associations between severe COVID-19 and diseases of seven organs: a proteome-wide mendelian randomization study.

The coronavirus disease 2019 (COVID-19) pandemic poses an enormous threat to public health worldwide. Many retrospective studies and case reports to date have shown associations between severe COVID-19 and diseases of multi-organs. However, the research on the causal mechanisms behind this phenomenon is neither extensive nor comprehensive. We conducted a proteome-wide Mendelian randomization (MR) study using summary statistics from a Genome-Wide Association Studies (GWAS) of severe COVID-19 and diseases related to seven organs: lung, spleen, liver, heart, kidney, testis, and thyroid, based on the European ancestry. The primary analytical method used is the radial inverse variance-weighted (radial IVW) method, supplemented with the inverse variance-weighted (IVW), weighted-median (WM), MR-Egger methods. Our findings have confirmed the association between severe COVID-19 and multiple organ-related diseases, such as Hypothyroidism, strict autoimmune (HTCBSA), Thyroid disorders (TD), and Graves' disease (GD). And we have also identified certain proteins that are associated with organ-related diseases, such as Superoxide Dismutase 2 (SOD2) and TEK Receptor Tyrosine Kinase (TEK), which are also considered potential drug targets. Phenotype scanning and sensitivity analyses were implemented to consolidate the results for Mendelian randomization. This study provides a compelling foundation for investigating COVID-19 caused diseases in future studies.

The remodeling of ovarian function: targeted delivery strategies for mesenchymal stem cells and their derived extracellular vesicles.

Premature ovarian insufficiency (POI) is an essential cause of reduced fertility and quality of life in young women. Mesenchymal stem cells (MSCs) and MSCs-derived extracellular vesicles (EVs) have the ability to migrate to damaged tissues and are considered as promising therapeutic approaches for POI. However, the homing ability and therapeutic efficacy of MSCs administered in vivo are still insufficient, and their potential tumorigenicity and multi-differentiation potential also bring many doubts about their safety. The targeting ability and migration efficiency of MSCs can be improved by genetic engineering and surface modification, thereby maximizing their therapeutic efficacy. However, the use of viral vectors also has increased safety concerns. In addition, EVs, which seem to be the current therapeutic alternative to MSCs, are still poorly targeted for distribution, although they have improved in terms of safety. This paper reviews the comparative therapeutic effects of MSCs and their derived EVs on POI, their biodistribution after in vivo administration, and the most important possible ovarian targeting strategies. Difficulties such as homogeneity and yield before clinical application are also discussed. This article will provide new insights into precision therapy and targeted drug delivery for female ovarian diseases.

Enhancing Oocyte Quality in Aging Mice: Insights from Mesenchymal Stem Cell Therapy and FOXO3a Signaling Pathway Activation.

Ovarian aging reduced the quality of oocytes, resulting in age-related female infertility. It is reported that mesenchymal stem cells (MSCs) therapy can improve age-related ovarian function decline and the success rate of in vitro maturation (IVM) in assisted reproductive therapy. In order to investigate the effectiveness and mechanisms of MSCs to enhance oocyte quality of cumulus oocyte complexes (COCs) in advanced age, this study focus on the respective functional improvement of oocytes and granulosa cells (GCs) from aging mice and further to explore and verify the possible mechanisms. Here, we studied a popular but significant protein of follicular development, Forkhead box O-3a (FOXO3a), which is a transcription factor that mediates a variety of cellular processes, but the functions of which in regulating oocyte quality in MSCs therapy still remain inconclusive. In this study, the RNA-seq data of metaphase II (MII) oocytes and GCs isolated from COCs confirmed that, GCs of immature follicles show the most potential to be the targeted cells of bone marrow mesenchymal stem cells (BMSCs) by FOXO3a signaling pathway. Furthermore, we demonstrated the effectiveness of BMSCs co-culture with aging COCs to enhance oocyte quality and found its mechanism to function via ameliorating the biological function of GCs by alleviating FOXO3a levels. These results provide significant fundamental research on MSCs therapy on ovarian aging, as well as offering guidance for raising the success rate of assisted reproductive technology such IVM in clinical and non-clinical settings.

The evolutionary innovation of root suberin lamellae contributed to the rise of seed plants.

Seed plants overtook ferns to become the dominant plant group during the late Carboniferous, a period in which the climate became colder and dryer1,2. However, the specific innovations driving the success of seed plants are not clear. Here we report that the appearance of suberin lamellae (SL) contributed to the rise of seed plants. We show that the Casparian strip and SL vascular barriers evolved at different times, with the former originating in the most recent common ancestor (MRCA) of vascular plants and the latter in the MRCA of seed plants. Our results further suggest that most of the genes required for suberin formation arose through gene duplication in the MRCA of seed plants. We show that the appearance of the SL in the MRCA of seed plants enhanced drought tolerance through preventing water loss from the stele. We hypothesize that SL provide a decisive selective advantage over ferns in arid environments, resulting in the decline of ferns and the rise of gymnosperms. This study provides insights into the evolutionary success of seed plants and has implications for engineering drought-tolerant crops or fern varieties.

Real-time complex light field generation through a multi-core fiber with deep learning.

The generation of tailored complex light fields with multi-core fiber (MCF) lensless microendoscopes is widely used in biomedicine. However, the computer-generated holograms (CGHs) used for such applications are typically generated by iterative algorithms, which demand high computation effort, limiting advanced applications like fiber-optic cell manipulation. The random and discrete distribution of the fiber cores in an MCF induces strong spatial aliasing to the CGHs, hence, an approach that can rapidly generate tailored CGHs for MCFs is highly demanded. We demonstrate a novel deep neural network-CoreNet, providing accurate tailored CGHs generation for MCFs at a near video rate. The CoreNet is trained by unsupervised learning and speeds up the computation time by two magnitudes with high fidelity light field generation compared to the previously reported CGH algorithms for MCFs. Real-time generated tailored CGHs are on-the-fly loaded to the phase-only spatial light modulator (SLM) for near video-rate complex light fields generation through the MCF microendoscope. This paves the avenue for real-time cell rotation and several further applications that require real-time high-fidelity light delivery in biomedicine.

Learned end-to-end high-resolution lensless fiber imaging towards real-time cancer diagnosis.

Recent advances in label-free histology promise a new era for real-time diagnosis in neurosurgery. Deep learning using autofluorescence is promising for tumor classification without histochemical staining process. The high image resolution and minimally invasive diagnostics with negligible tissue damage is of great importance. The state of the art is raster scanning endoscopes, but the distal lens optics limits the size. Lensless fiber bundle endoscopy offers both small diameters of a few 100 microns and the suitability as single-use probes, which is beneficial in sterilization. The problem is the inherent honeycomb artifacts of coherent fiber bundles (CFB). For the first time, we demonstrate an end-to-end lensless fiber imaging with exploiting the near-field. The framework includes resolution enhancement and classification networks that use single-shot CFB images to provide both high-resolution imaging and tumor diagnosis. The well-trained resolution enhancement network not only recovers high-resolution features beyond the physical limitations of CFB, but also helps improving tumor recognition rate. Especially for glioblastoma, the resolution enhancement network helps increasing the classification accuracy from 90.8 to 95.6%. The novel technique enables histological real-time imaging with lensless fiber endoscopy and is promising for a quick and minimally invasive intraoperative treatment and cancer diagnosis in neurosurgery.

Aging conundrum: A perspective for ovarian aging.

Progressive loss of physiological integrity and accumulation of degenerative changes leading to functional impairment and increased susceptibility to diseases are the main features of aging. The ovary, the key organ that maintains female reproductive and endocrine function, enters aging earlier and faster than other organs and has attracted extensive attention from society. Ovarian aging is mainly characterized by the progressive decline in the number and quality of oocytes, the regulatory mechanisms of which have yet to be systematically elucidated. This review discusses the hallmarks of aging to further highlight the main characteristics of ovarian aging and attempt to explore its clinical symptoms and underlying mechanisms. Finally, the intervention strategies related to aging are elaborated, especially the potential role of stem cells and cryopreservation of embryos, oocytes, or ovarian tissue in the delay of ovarian aging.