Complete genome assembly provides a high-quality skeleton for pan-NLRome construction in melon.

Melon (Cucumis melo L.), being under intensive domestication and selective breeding, displays an abundant phenotypic diversity. Wild germplasm with tolerance to stress represents an untapped genetic resource for discovery of disease-resistance genes. To comprehensively characterize resistance genes in melon, we generate a telomere-to-telomere (T2T) and gap-free genome of wild melon accession PI511890 (C. melo var. chito) with a total length of 375.0 Mb and a contig N50 of 31.24 Mb. The complete genome allows us to dissect genome architecture and identify resistance gene analogs. We construct a pan-NLRome using seven melon genomes, which include 208 variable and 18 core nucleotide-binding leucine-rich repeat receptors (NLRs). Multiple disease-related transcriptome analyses indicate that most up-regulated NLRs induced by pathogens are shell or cloud NLRs. The T2T gap-free assembly and the pan-NLRome not only serve as essential resources for genomic studies and molecular breeding of melon but also provide insights into the genome architecture and NLR diversity.

Integrated full-length transcriptome and metabolome analysis reveals the defence response of melon to gummy stem blight.

Gummy stem blight (GSB), a widespread disease causing great loss to cucurbit production, has become a major threat to melon cultivation. However, the melon-GSB interaction remains largely unknown. Here, full-length transcriptome and widely targeted metabolome were used to investigate the defence responses of resistant (PI511089) and susceptible (Payzawat) melon accessions to GSB pathogen infection at 24 h. The biosynthesis of secondary metabolites and MAPK signalling pathway were specifically enriched for differentially expressed genes in PI511890, while carbohydrate metabolism and amino acid metabolism were specifically enriched in Payzawat. More than 1000 novel genes were identified and MAPK signalling pathway was specifically enriched for them in PI511890. There were 11 793 alternative splicing events involving in the defence response to GSB. Totally, 910 metabolites were identified in Payzawat and PI511890, and flavonoids were the dominant metabolites. Integrated full-length transcriptome and metabolome analysis showed eriodictyol and oxalic acid were the potential marker metabolites for GSB resistance in melon. Moreover, posttranscription regulation was widely involved in the defence response of melon to GSB pathogen infection. These results not only improve our understanding on the interaction between melon and GSB, but also facilitate the genetic improvement of melon with GSB resistance.

Deep learning-enhanced snapshot hyperspectral confocal microscopy imaging system.

Laser-scanning confocal hyperspectral microscopy is a powerful technique to identify the different sample constituents and their spatial distribution in three-dimensional (3D). However, it suffers from low imaging speed because of the mechanical scanning methods. To overcome this challenge, we propose a snapshot hyperspectral confocal microscopy imaging system (SHCMS). It combined coded illumination microscopy based on a digital micromirror device (DMD) with a snapshot hyperspectral confocal neural network (SHCNet) to realize single-shot confocal hyperspectral imaging. With SHCMS, high-contrast 160-bands confocal hyperspectral images of potato tuber autofluorescence can be collected by only single-shot, which is almost 5 times improvement in the number of spectral channels than previously reported methods. Moreover, our approach can efficiently record hyperspectral volumetric imaging due to the optical sectioning capability. This fast high-resolution hyperspectral imaging method may pave the way for real-time highly multiplexed biological imaging.

Molecular mapping for fruit-related traits, and joint identification of candidate genes and selective sweeps for seed size in melon.

Melon is a popular fruit vegetable crop worldwide with diverse morphological variation. We report a high-density genetic map of melon and nine major QTLs with physical region ranging from 43.47 kb to 1.89 Mb. Importantly, two seed-related trait QTLs were repeatedly detected in two environments, and the mapping region was narrowed to 522 kb according to a regional linkage analysis. A total of 40 annotated genes were screened for nonsynonymous variations, of which EVM0009818, involved in cytokinin-activated signaling, was differentially expressed in the young fruits of parents based on RNA-seq. Selective sweep analysis identified 152 sweep signals for seed size, including the two seed-related QTLs and nine homologs that have been verified to regulate seed size in Arabidopsis or rice. This work illustrates the power of a joint analysis combining resequencing-based genetic map for QTL mapping and a combination of KASP genotyping and RNA-seq analysis to facilitate QTL fine mapping.

Ptychography-based high-throughput lensless on-chip microscopy via incremental proximal algorithms.

Ptychography-based lensless on-chip microscopy enables high-throughput imaging by retrieving the missing phase information from intensity measurements. Numerous reconstruction algorithms for ptychography have been proposed, yet only a few incremental algorithms can be extended to lensless on-chip microscopy because of large-scale datasets but limited computational efficiency. In this paper, we propose the use of accelerated proximal gradient methods for blind ptychographic phase retrieval in lensless on-chip microscopy. Incremental gradient approaches are adopted in the reconstruction routine. Our algorithms divide the phase retrieval problem into sub-problems involving the evaluation of proximal operator, stochastic gradient descent, and Wirtinger derivatives. We benchmark the performances of accelerated proximal gradient, extended ptychographic iterative engine, and alternating direction method of multipliers, and discuss their convergence and accuracy in both noisy and noiseless cases. We also validate our algorithms using experimental datasets, where full field of view measurements are captured to recover the high-resolution complex samples. Among these algorithms, accelerated proximal gradient presents the overall best performance regarding accuracy and convergence rate. The proposed methods may find applications in ptychographic reconstruction, especially for cases where a wide field of view and high resolution are desired at the same time.

Near-field Fourier ptychography: super-resolution phase retrieval via speckle illumination.

High spatial resolution is the goal of many imaging systems. While designing a high-resolution lens with diffraction-limited performance over a large field of view remains a difficult task, creating a complex speckle pattern with wavelength-limited spatial features is easily accomplished with a simple random diffuser. With this observation and the concept of near-field ptychography, we report a new imaging modality, termed near-field Fourier ptychography, to address high-resolution imaging challenges in both microscopic and macroscopic imaging settings. 'Near-field' refers to placing the object at a short defocus distance with a large Fresnel number. We project a speckle pattern with fine spatial features on the object instead of directly resolving the spatial features via a high-resolution lens. We then translate the object (or speckle) to different positions and acquire the corresponding images by using a low-resolution lens. A ptychographic phase retrieval process is used to recover the complex object, the unknown speckle pattern, and the coherent transfer function at the same time. In a microscopic imaging setup, we use a 0.12 numerical aperture (NA) lens to achieve an NA of 0.85 in the reconstruction process. In a macroscale photographic imaging setup, we achieve ~7-fold resolution gain by using a photographic lens. The collection optics do not determine the final achievable resolution; rather, the speckle pattern's feature size does. This is similar to our recent demonstration in fluorescence imaging settings (Guo et al., Biomed. Opt. Express, 9(1), 2018). The reported imaging modality can be employed in light, coherent X-ray, and transmission electron imaging systems to increase resolution and provide quantitative absorption and object phase contrast.

Rapid and robust two-dimensional phase unwrapping via deep learning.

Two-dimensional phase unwrapping algorithms are widely used in optical metrology and measurements. The high noise from interference measurements, however, often leads to the failure of conventional phase unwrapping algorithms. In this paper, we propose a deep convolutional neural network (DCNN) based method to perform rapid and robust two-dimensional phase unwrapping. In our approach, we employ a DCNN architecture, DeepLabV3+, with noise suppression and strong feature representation capabilities. The employed DCNN is first used to perform semantic segmentation to obtain the segmentation result of the wrapped phase map. We then combine the wrapped phase map with the segmentation result to generate the unwrapped phase. We benchmarked our results by comparing them with well-established methods. The reported approach out-performed the conventional path-dependent and path-independent algorithms. We also tested the robustness of the reported approach using interference measurements from optical metrology setups. Our results, again, clearly out-performed the conventional phase unwrap algorithms. The reported approach may find applications in optical metrology and microscopy imaging.

[Research on the Whole-process Cloud Monitoring Mode of in Vitro Diagnostic Medical Devices Adverse Events].

OBJECTIVE: To improve the monitoring mode of in vitro diagnostic medical devices adverse events. METHODS: By discussing the objective laws of the characteristics, performances and causes of in vitro diagnostic medical devices adverse events, the key points of monitoring work were clarified. RESULTS: The whole-process cloud monitoring mode for adverse events of in vitro diagnostic medical devices was constructed based on risk management, and the working procedures for the four core links i.e. collection and report, investigation, analysis and evaluation, and controlling were formulated. CONCLUSIONS: The whole-process cloud monitoring mode contributes to improve the monitoring level and efficiency of in vitro diagnostic medical devices adverse events in China, so as to strengthen risk control capability and ensure the public can use medical devices safely.

Platelet-derived growth factor subunit B is required for tendon-bone healing using bone marrow-derived mesenchymal stem cells after rotator cuff repair in rats.

As a common cause of shoulder pain and disability, rotator cuff injury (RCI) represents a debilitating condition affecting an individual's quality of life. Although surgical repair has been shown to be somewhat effective, many patients may still suffer from reduced shoulder function. The aim of the current study was to identify a more effective mode of RCI treatment by investigating the effect of platelet-derived growth factor subunit B (PDGF-B) on tendon-bone healing after RCI repair by modifying bone marrow-derived mesenchymal stem cells (BMSCs). Surface markers of BMSCs were initially detected by means of flow cytometry, followed by establishment of the rat models and construction of the lentiviral vector. 3-(4,5-dimethyl-2-thiazolyl)-2,5-diphenyl-2-H-tetrazolium bromide, Thiazolyl Blue Tetrazolium Bromide (MTT) assay, alizarin red staining, and oil red O staining were used to provide verification that PDGF-B was indeed capable of promoting BMSC viability, osteogenic and adipogenic differentiation capability. Furthermore, biomechanical assessment results indicated that PDGF-B could increase the ultimate load and stiffness of the tendon tissue. Real-time reverse-transcription quantitative polymerase chain reaction and Western blot analysis methods provided evidence suggesting that PDGF-B facilitated the expression of tendon-bone healing-related genes and proteins, while contrasting results were obtained after PDGF-B silencing. Taken together, the key findings of the current study provided evidence suggesting that overexpressed PDGF-B could act to enhance tendon-bone healing after RCI repair, thus highlighting the potential of the functional promotion of PDGF-B as a future RCI therapeutic approach.

Neuroprotective effect of Trichosanthes kirilowii cassia twig on cerebral ischemia-reperfusion injury in rats.

In this study, in-depth observation and investigation of blood-brain barrier permeability and neuroprotective effect of Trichosanthes kirilowii cassia twig particles on rats with cerebral ischemia-reperfusion injury were performed. Focal cerebral ischemia-reperfusion injury model was established by middle cerebral artery occlusion method, reperfusion was implemented 2 hours after ischemia; qualitative analysis and investigation of trichosanthes kirilowii cassia twig particles in plasma, brain tissue and cerebrospinal fluid in normal and middle cerebral artery occlusion (MCAO) rats were done by high-performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS); changes in neurological deficits, cerebral infarction stereometry, blood-brain barrier permeability and histopathological changes of MCAO model rats were observed. Qualitative analysis by HPLC-MS/MS results showed that ingredients, paeoniflorin, albiflorin, liquiritin in Trichosanthes kirilowii cassia twig particles can reach the brain through the blood-brain barrier. In the model group, glycyrrhizin and glycyrrhizic acid can be detected in brain tissue or cerebrospinal fluid. In addition, Trichosanthes kirilowii cassia twig particles can significantly lower neurological deficits of rats in middle cerebral artery occlusion model, reduce the Evans blue penetration, contract infarct size, and reduce pathological tissue injury of cerebral ischemia reperfusion. The ingredients of Trichosanthes kirilowii cassia twig particles can reach the brain tissue through the blood-brain barrier and play a role in neuroprotection of rats with cerebral ischemia-reperfusion injury, which has important research significance and brings scientific experimental, theoretical basis for clinical drug use.

Fourier ptychographic microscopy using a generalized Anscombe transform approximation of the mixed Poisson-Gaussian likelihood.

Fourier ptychographic microscopy (FPM) is a novel computational microscopy technique that provides intensity images with both wide field-of-view (FOV) and high-resolution (HR). By combining ideas from synthetic aperture and phase retrieval, FPM iteratively stitches together a number of variably illuminated, low-resolution (LR) intensity images in Fourier space to reconstruct an HR complex sample image. In practice, however, the reconstruction of FPM is sensitive to the input noise, including Gaussian noise, Poisson shot noise or mixed Poisson-Gaussian noise. To efficiently address these noises, we developed a novel FPM reconstruction method termed generalized Anscombe transform approximation Fourier ptychographic (GATFP) reconstruction. The method utilizes the generalized Anscombe transform (GAT) approximation for the noise model, and a maximum likelihood theory is employed for formulating the FPM optimization problem. We validated the proposed method with both simulated data for quantitative evaluation and real experimental data captured using FPM setup. The results show that the proposed method achieves state-of-the-art performance in comparison with other approaches.

Nonlinear optimization approach for Fourier ptychographic microscopy.

Fourier ptychographic microscopy (FPM) is recently proposed as a computational imaging method to bypass the limitation of the space-bandwidth product of the traditional optical system. It employs a sequence of low-resolution images captured under angularly varying illumination and applies the phase retrieval algorithm to iteratively reconstruct a wide-field, high-resolution image. In current FPM imaging system, system uncertainties, such as the pupil aberration of the employed optics, may significantly degrade the quality of the reconstruction. In this paper, we develop and test a nonlinear optimization algorithm to improve the robustness of the FPM imaging system by simultaneously considering the reconstruction and the system imperfections. Analytical expressions for the gradient of a squared-error metric with respect to the object and illumination allow joint optimization of the object and system parameters. The algorithm achieves superior reconstructions when the system parameters are inaccurately known or in the presence of noise and corrects the pupil aberrations simultaneously. Experiments on both synthetic and real captured data validate the effectiveness of the proposed method.

Self-learning based Fourier ptychographic microscopy.

Fourier Ptychographic Microscopy (FPM) is a newly proposed computational imaging method aimed at reconstructing a high-resolution wide-field image from a sequence of low-resolution images. These low-resolution images are captured under varied illumination angles and the FPM recovery routine then stitches them together in the Fourier domain iteratively. Although FPM has achieved success with static sample reconstructions, the long acquisition time inhibits real-time application. To address this problem, we propose here a self-learning based FPM which accelerates the acquisition and reconstruction procedure. We first capture a single image under normally incident illumination, and then use it to simulate the corresponding low-resolution images under other illumination angles. The simulation is based on the relationship between the illumination angles and the shift of the sample's spectrum. We analyze the importance of the simulated low-resolution images in order to devise a selection scheme which only collects the ones with higher importance. The measurements are then captured with the selection scheme and employed to perform the FPM reconstruction. Since only measurements of high importance are captured, the time requirements of data collection as well as image reconstruction can be greatly reduced. We validate the effectiveness of the proposed method with simulation and experimental results showing that the reduction ratio of data size requirements can reach over 70%, without sacrificing image reconstruction quality.

Immunomodulatory effects of Taishan Pinus massoniana pollen polysaccharide and propolis on immunosuppressed chickens.

Co-infection of reticuloendotheliosis virus (REV) and avian leukosis virus subgroup J (ALV-J), which can cause suppressed immunity and vaccination failure, frequently occurs in chicken flocks in China. Taishan Pinus massoniana pollen polysaccharide (TPPPS) and propolis (PP) have been proven to possess immune modulatory effects and improve the immune effects of vaccines. This study aimed to investigate the immune modulatory ability of TPPPS and PP on chickens co-infected with immunosuppressive viruses. Prior to the study, chickens were artificially established as REV and ALV-J co-infection models. Four randomly assigned groups of these immunosuppressed chickens were successively administered with TPPPS, PP, mixture of TPPPS and PP (TPPPS-PP), or phosphate-buffered saline (PBS) for three days. At nine days old, the four immunosuppressed groups, as well as one normal group, were inoculated with the attenuated Newcastle disease (ND) vaccine. During the monitoring period, the indices of immune organ weight, lymphocyte transformation rates, CD4(+) and CD8(+) T-lymphocyte counts in peripheral blood, IL-2 and IFN-γ secretions, serum antibody titers of ND vaccine, and viral loads in spleens were determined. The results showed that chickens administered with TPPPS, PP, or TPPPS-PP could significantly enhance the levels of the above immune parameters compared to chickens in the PBS group. We observed the strongest immunity in the TPPPS-PP group, which indicates that the combination of TPPPS and PP versus TPPPS or PP alone, could generate better effects on improving the immune system effectiveness of immunosuppressed chickens.

Protective immunity induced by the vaccination of recombinant Proteus mirabilis OmpA expressed in Pichia pastoris.

Proteus mirabilis (P. mirabilis) is a zoonotic pathogen that has recently presented a rising infection rate in the poultry industry. To develop an effective vaccine to protect chickens against P. mirabilis infection, OmpA, one of the major outer membrane proteins of P. mirabilis, was expressed in Pichia pastoris. The concentration of the expressed recombinant OmpA protein reached 8.0µg/mL after induction for 96h with 1.0% methanol in the culture. In addition, OmpA protein was confirmed by SDS-PAGE and Western blot analysis using the antibody against Escherichia coli-expressed OmpA protein. Taishan Pinus massoniana pollen polysaccharide, a known plant-derived adjuvant, was mixed into the recombinant OmpA protein to prepare the OmpA subunit vaccine. We then subcutaneously inoculated this vaccine into chickens to examine the immunoprotective effects. ELISA analysis indicated that an excellent antibody response against OmpA was elicited in the vaccinated chickens. Moreover, a high protection rate of 80.0% was observed in the vaccinated group, which was subsequently challenged with P. mirabilis. The results suggest that the eukaryotic P. mirabilis OmpA was an ideal candidate protein for developing an effective subunit vaccine against P. mirabilis infection.

Co-adjuvant effects of plant polysaccharide and propolis on chickens inoculated with Bordetella avium inactivated vaccine.

Taishan Pinus massoniana pollen polysaccharide (TPPPS), propolis (PP) and aloe polysaccharide (AP), used as adjuvants, have been proven to possess immunity-enhancing functions. However, their collaborative immunomodulatory effects are largely unknown. To determine which combination can induce the best effects, the three adjuvants were separately or conjointly added into Bordetella avium inactivated vaccines to investigate their co-adjuvant effects on vaccinated chickens. We found that, among all six adjuvant-treated vaccine inoculated groups (TPPPS, PP, AP, TPPPS-PP, PP-AP and TPPPS-AP), the chickens inoculated with TPPPS, PP or TPPPS-PP adjuvant vaccines showed significantly higher levels of antibody titre, cytokine, lymphocyte transformation and peripheral blood T-lymphocyte count than those of non-adjuvant vaccine inoculated groups (P < 0.05), indicating the good immune-enhancing effects of TPPPS and PP. The TPPPS-PP group showed the highest levels of antibody titres and interleukin-2 (IL-2) at 14-28 days post the first inoculation (dpi), lymphocyte transformation rates (LTRs) at 14-35 dpi, CD4(+) T-lymphocyte counts at 14-42 dpi, and CD8(+) T-lymphocyte counts at 28 dpi. The results revealed that B. avium inactivated vaccine used conjointly with TPPPS and PP induced the strongest humoral and cellular immune responses. Thus, there was a synergistic effect between TPPPS and PP on enhancing immunity, which suggests that they can be used as a novel adjuvant formulation for the development of poultry vaccines.

A two-stage fine-tuning method for low-resource cross-lingual summarization.

Cross-lingual summarization (CLS) is the task of condensing lengthy source language text into a concise summary in a target language. This presents a dual challenge, demanding both cross-language semantic understanding (i.e., semantic alignment) and effective information compression capabilities. Traditionally, researchers have tackled these challenges using two types of methods: pipeline methods (e.g., translate-then-summarize) and end-to-end methods. The former is intuitive but prone to error propagation, particularly for low-resource languages. The later has shown an impressive performance, due to multilingual pre-trained models (mPTMs). However, mPTMs (e.g., mBART) are primarily trained on resource-rich languages, thereby limiting their semantic alignment capabilities for low-resource languages. To address these issues, this paper integrates the intuitiveness of pipeline methods and the effectiveness of mPTMs, and then proposes a two-stage fine-tuning method for low-resource cross-lingual summarization (TFLCLS). In the first stage, by recognizing the deficiency in the semantic alignment for low-resource languages in mPTMs, a semantic alignment fine-tuning method is employed to enhance the mPTMs' understanding of such languages. In the second stage, while considering that mPTMs are not originally tailored for information compression and CLS demands the model to simultaneously align and compress, an adaptive joint fine-tuning method is introduced. This method further enhances the semantic alignment and information compression abilities of mPTMs that were trained in the first stage. To evaluate the performance of TFLCLS, a low-resource CLS dataset, named Vi2ZhLow, is constructed from scratch; moreover, two additional low-resource CLS datasets, En2ZhLow and Zh2EnLow, are synthesized from widely used large-scale CLS datasets. Experimental results show that TFCLS outperforms state-of-the-art methods by 18.88%, 12.71% and 16.91% in ROUGE-2 on the three datasets, respectively, even when limited with only 5,000 training samples.

Unsupervised Multi-Domain Progressive Stain Transfer Guided by Style Encoding Dictionary.

In histopathology, the tissue slides are usually stained by common H&E stain or special stains (MAS, PAS, and PASM, etc.) to clearly show specific tissue structures. The rapid development of deep learning provides a good solution to generate virtual staining images to significantly reduce the time and labor costs associated with histochemical staining. However, most existing methods need to train a special model for every two stains, which consumes a lot of computing resources with the increasing of staining types. To address this problem, we propose an unsupervised multi-domain stain transfer method, GramGAN, which realizes the progressive transfer through cascaded Style-Guided blocks. For each Style-Guided block, we design a style encoding dictionary to characterize and store all the staining style information. In addition, we propose a Rényi entropy-based regularization term to improve the discrimination ability of different styles. The experimental results show that our method can realize accurate transferring among multiple staining styles with better performance. Furthermore, we build and publish a special stained image dataset suitable for glomeruli segmentation (including H&E staining), where the accuracy of glomeruli detection and segmentation can be significantly improved after transferring H&E-stained images to PAS-stained and PASM-stained ones by our method. The code is publicly available at: https://github.com/xianchaoguan/GramGAN.

Domain generalization across tumor types, laboratories, and species - Insights from the 2022 edition of the Mitosis Domain Generalization Challenge.

Recognition of mitotic figures in histologic tumor specimens is highly relevant to patient outcome assessment. This task is challenging for algorithms and human experts alike, with deterioration of algorithmic performance under shifts in image representations. Considerable covariate shifts occur when assessment is performed on different tumor types, images are acquired using different digitization devices, or specimens are produced in different laboratories. This observation motivated the inception of the 2022 challenge on MItosis Domain Generalization (MIDOG 2022). The challenge provided annotated histologic tumor images from six different domains and evaluated the algorithmic approaches for mitotic figure detection provided by nine challenge participants on ten independent domains. Ground truth for mitotic figure detection was established in two ways: a three-expert majority vote and an independent, immunohistochemistry-assisted set of labels. This work represents an overview of the challenge tasks, the algorithmic strategies employed by the participants, and potential factors contributing to their success. With an F1 score of 0.764 for the top-performing team, we summarize that domain generalization across various tumor domains is possible with today's deep learning-based recognition pipelines. However, we also found that domain characteristics not present in the training set (feline as new species, spindle cell shape as new morphology and a new scanner) led to small but significant decreases in performance. When assessed against the immunohistochemistry-assisted reference standard, all methods resulted in reduced recall scores, with only minor changes in the order of participants in the ranking.

Analysis of Shear Performance of Multi-Bolt Shear Connectors.

Bolt shear connectors used in prefabricated steel-concrete composite beams can be arranged into a group to enhance the construction efficiency, which will cause the multi-bolt effect and further affect the shear performance of bolt connectors. This paper developed three-dimensional finite element models of push-out specimens to investigate the shear performance of multi-bolt connectors. Numerical results showed that the friction coefficient at the interfaces between the steel girders and precast concrete (PC) slabs and bolt preload dramatically improved the initial stiffness of bolts; when the longitudinal spacing of bolts was reduced from 100 mm to 60 mm, the decrease in the average peak load per bolt was 3.5%, 9.2%, and 11.4% for bolts of 16 mm, 20 mm, and 24 mm diameters. A modified calculation method for the shear resistance of multi-bolt shear connectors was proposed based on the numerical analysis, and a simplified model of shear load versus relative slip was further developed.