CoF-DResNet: Cancer Metastasis Recognition Network based on Dynamic Coordinated Metabolic Attention and Structural Attention.

BACKGROUND: Cancer metastasis usually means that cancer cells spread to other tissues or organs, and the condition worsens. Identifying whether cancer has metastasized can help doctors infer the progression of a patient's condition and is an essential prerequisite for devising treatment plans. Fluorine 18 fluorodeoxyglucose positron emission tomography/computed tomography ( 18F -FDG PET/CT) is an advanced cancer diagnostic imaging technique that provides both metabolic and structural information. METHOD: In cancer metastasis recognition tasks, effectively integrating metabolic and structural information stands as a key technology to enhance feature representation and recognition performance. This paper proposes a cancer metastasis identification network based on dynamic coordinated metabolic attention and structural attention to address these challenges. Specifically, metabolic and structural features are extracted by incorporating a dynamic coordinated attention module (DCAM) into two branches of ResNet networks, thereby amalgamating high metabolic spatial information from PET images with texture structure information from CT images, and dynamically adjusting this process through iterations. DISCUSSION: Next, to improve the efficacy of feature expression, a multi-receptive field feature fusion module (MRFM) is included in order to execute multi-receptive field fusion of semantic features. RESULT: To validate the effectiveness of our proposed model, experiments were conducted on both a private lung lymph nodes dataset and a public soft tissue sarcomas dataset. CONCLUSION: The accuracy of our method reached 76.0% and 75.1% for the two datasets, respectively, demonstrating an improvement of 6.8% and 5.6% compared to ResNet, thus affirming the efficacy of our method.

A multi-instance tumor subtype classification method for small PET datasets using RA-DL attention module guided deep feature extraction with radiomics features.

BACKGROUND: Positron emission tomography (PET) is extensively employed for diagnosing and staging various tumors, including liver cancer, lung cancer, and lymphoma. Accurate subtype classification of tumors plays a crucial role in formulating effective treatment plans for patients. Notably, lymphoma comprises subtypes like diffuse large B-cell lymphoma and Hodgkin's lymphoma, while lung cancer encompasses adenocarcinoma, small cell carcinoma, and squamous cell carcinoma. Similarly, liver cancer consists of subtypes such as cholangiocarcinoma and hepatocellular carcinoma. Consequently, the subtype classification of tumors based on PET images holds immense clinical significance. However, in clinical practice, the number of cases available for each subtype is often limited and imbalanced. Therefore, the primary challenge lies in achieving precise subtype classification using a small dataset. METHOD: This paper presents a novel approach for tumor subtype classification in small datasets using RA-DL (Radiomics-DeepLearning) attention. To address the limited sample size, Support Vector Machines (SVM) is employed as the classifier for tumor subtypes instead of deep learning methods. Emphasizing the importance of texture information in tumor subtype recognition, radiomics features are extracted from the tumor regions during the feature extraction stage. These features are compressed using an autoencoder to reduce redundancy. In addition to radiomics features, deep features are also extracted from the tumors to leverage the feature extraction capabilities of deep learning. In contrast to existing methods, our proposed approach utilizes the RA-DL-Attention mechanism to guide the deep network in extracting complementary deep features that enhance the expressive capacity of the final features while minimizing redundancy. To address the challenges of limited and imbalanced data, our method avoids using classification labels during deep feature extraction and instead incorporates 2D Region of Interest (ROI) segmentation and image reconstruction as auxiliary tasks. Subsequently, all lesion features of a single patient are aggregated into a feature vector using a multi-instance aggregation layer. RESULT: Validation experiments were conducted on three PET datasets, specifically the liver cancer dataset, lung cancer dataset, and lymphoma dataset. In the context of lung cancer, our proposed method achieved impressive performance with Area Under Curve (AUC) values of 0.82, 0.84, and 0.83 for the three-classification task. For the binary classification task of lymphoma, our method demonstrated notable results with AUC values of 0.95 and 0.75. Moreover, in the binary classification task of liver tumor, our method exhibited promising performance with AUC values of 0.84 and 0.86. CONCLUSION: The experimental results clearly indicate that our proposed method outperforms alternative approaches significantly. Through the extraction of complementary radiomics features and deep features, our method achieves a substantial improvement in tumor subtype classification performance using small PET datasets.

ECPC-IDS: A benchmark endometrial cancer PET/CT image dataset for evaluation of semantic segmentation and detection of hypermetabolic regions.

BACKGROUND: Endometrial cancer is one of the most common tumors in the female reproductive system and is the third most common gynecological malignancy that causes death after ovarian and cervical cancer. Early diagnosis can significantly improve the 5-year survival rate of patients. With the development of artificial intelligence, computer-assisted diagnosis plays an increasingly important role in improving the accuracy and objectivity of diagnosis and reducing the workload of doctors. However, the absence of publicly available image datasets restricts the application of computer-assisted diagnostic techniques. METHODS: In this paper, a publicly available Endometrial Cancer PET/CT Image Dataset for Evaluation of Semantic Segmentation and Detection of Hypermetabolic Regions (ECPC-IDS) are published. Specifically, the segmentation section includes PET and CT images, with 7159 images in multiple formats totally. In order to prove the effectiveness of segmentation on ECPC-IDS, six deep learning semantic segmentation methods are selected to test the image segmentation task. The object detection section also includes PET and CT images, with 3579 images and XML files with annotation information totally. Eight deep learning methods are selected for experiments on the detection task. RESULTS: This study is conduct using deep learning-based semantic segmentation and object detection methods to demonstrate the distinguishability on ECPC-IDS. From a separate perspective, the minimum and maximum values of Dice on PET images are 0.546 and 0.743, respectively. The minimum and maximum values of Dice on CT images are 0.012 and 0.510, respectively. The target detection section's maximum mAP values on PET and CT images are 0.993 and 0.986, respectively. CONCLUSION: As far as we know, this is the first publicly available dataset of endometrial cancer with a large number of multi-modality images. ECPC-IDS can assist researchers in exploring new algorithms to enhance computer-assisted diagnosis, benefiting both clinical doctors and patients. ECPC-IDS is also freely published for non-commercial at: https://figshare.com/articles/dataset/ECPC-IDS/23808258.

PST-Radiomics: a PET/CT lymphoma classification method based on pseudo spatial-temporal radiomic features and structured atrous recurrent convolutional neural network.

Objective.Existing radiomic methods tend to treat each isolated tumor as an inseparable whole, when extracting radiomic features. However, they may discard the critical intra-tumor metabolic heterogeneity (ITMH) information, that contributes to triggering tumor subtypes. To improve lymphoma classification performance, we propose a pseudo spatial-temporal radiomic method (PST-Radiomics) based on positron emission tomography computed tomography (PET/CT).Approach.Specifically, to enable exploitation of ITMH, we first present a multi-threshold gross tumor volume sequence (GTVS). Next, we extract 1D radiomic features based on PET images and each volume in GTVS and create a pseudo spatial-temporal feature sequence (PSTFS) tightly interwoven with ITMH. Then, we reshape PSTFS to create 2D pseudo spatial-temporal feature maps (PSTFM), of which the columns are elements of PSTFS. Finally, to learn from PSTFM in an end-to-end manner, we build a light-weighted pseudo spatial-temporal radiomic network (PSTR-Net), in which a structured atrous recurrent convolutional neural network serves as a PET branch to better exploit the strong local dependencies in PSTFM, and a residual convolutional neural network is used as a CT branch to exploit conventional radiomic features extracted from CT volumes.Main results.We validate PST-Radiomics based on a PET/CT lymphoma subtype classification task. Experimental results quantitatively demonstrate the superiority of PST-Radiomics, when compared to existing radiomic methods.Significance.Feature map visualization of our method shows that it performs complex feature selection while extracting hierarchical feature maps, which qualitatively demonstrates its superiority.

Analysis of bZIP gene family in lotus (Nelumbo) and functional study of NnbZIP36 in regulating anthocyanin synthesis.

BACKGROUND: The basic leucine zipper (bZIP) family is a predominant group of transcription factors in plants, involved in regulating plant growth, development, and response to stressors. Additionally, the bZIP gene family has a key role in anthocyanin production. Despite the significant role of bZIP genes in plants, their potential contribution in lotus remains understudied. RESULTS: A total of 124 bZIP genes (59 NnbZIPs and 65 NlbZIPs) were identified from genomes of two lotus species. These genes were classified into 13 groups according to the grouping principle of the Arabidopsis bZIP gene family. Analysis of promoter cis-acting elements indicated that most bZIP gene family members in lotus are associated with response to abiotic stresses. The promoters of some bZIP genes contain MYB binding sites that regulate anthocyanin synthesis. We examined the anthocyanin content of the petals from three different colored lotus, combined with transcriptome data analysis and qRT-PCR results, showing that the expression trends of NnbZIP36 and the homologous gene NlbZIP38 were significantly correlated with the anthocyanin content in lotus petals. Furthermore, we found that overexpression of NnbZIP36 in Arabidopsis promoted anthocyanin accumulation by upregulating the expression of genes (4CL, CHI, CHS, F3H, F3'H, DFR, ANS and UF3GT) related to anthocyanin synthesis. CONCLUSIONS: Our study enhances the understanding of the bZIP gene family in lotus and provides evidence for the role of NnbZIP36 in regulating anthocyanin synthesis. This study also sets the stage for future investigations into the mechanism by which the bZIP gene family regulates anthocyanin biosynthesis in lotus.

A Lightweight Network for Contextual and Morphological Awareness for Hepatic Vein Segmentation.

Accurate segmentation of the hepatic vein can improve the precision of liver disease diagnosis and treatment. Since the hepatic venous system is a small target and sparsely distributed, with various and diverse morphology, data labeling is difficult. Therefore, automatic hepatic vein segmentation is extremely challenging. We propose a lightweight contextual and morphological awareness network and design a novel morphology aware module based on attention mechanism and a 3D reconstruction module. The morphology aware module can obtain the slice similarity awareness mapping, which can enhance the continuous area of the hepatic veins in two adjacent slices through attention weighting. The 3D reconstruction module connects the 2D encoder and the 3D decoder to obtain the learning ability of 3D context with a very small amount of parameters. Compared with other SOTA methods, using the proposed method demonstrates an enhancement in the dice coefficient with few parameters on the two datasets. A small number of parameters can reduce hardware requirements and potentially have stronger generalization, which is an advantage in clinical deployment.

Transcriptome analysis reveals that PbMYB61 and PbMYB308 are involved in the regulation of lignin biosynthesis in pear fruit stone cells.

Pear fruit stone cells have thick walls and are formed by the secondary deposition of lignin in the primary cell wall of thin-walled cells. Their content and size seriously affect fruit characteristics related to edibility. To reveal the regulatory mechanism underlying stone cell formation during pear fruit development and to identify hub genes, we examined the stone cell and lignin contents of 30 'Shannongsu' pear flesh samples and analyzed the transcriptomes of 15 pear flesh samples collected at five developmental stages. On the basis of the RNA-seq data, 35 874 differentially expressed genes were detected. Additionally, two stone cell-related modules were identified according to a WGCNA. A total of 42 lignin-related structural genes were subsequently obtained. Furthermore, nine hub structural genes were identified in the lignin regulatory network. We also identified PbMYB61 and PbMYB308 as candidate transcriptional regulators of stone cell formation after analyzing co-expression networks and phylogenetic relationships. Finally, we experimentally validated and characterized the candidate transcription factors and revealed that PbMYB61 regulates stone cell lignin formation by binding to the AC element in the PbLAC1 promoter to upregulate expression. However, PbMYB308 negatively regulates stone cell lignin synthesis by binding to PbMYB61 to form a dimer that cannot activate PbLAC1 expression. In this study, we explored the lignin synthesis-related functions of MYB family members. The results presented herein are useful for elucidating the complex mechanisms underlying lignin biosynthesis during pear fruit stone cell development.

A Deep Learning Model for Automatic Segmentation of Intraparenchymal and Intraventricular Hemorrhage for Catheter Puncture Path Planning.

Intracerebral hemorrhage is the subtype of stroke with the highest mortality rate, especially when it also causes secondary intraventricular hemorrhage. The optimal surgical option for intracerebral hemorrhage remains one of the most controversial areas of neurosurgery. We aim to develop a deep learning model for the automatic segmentation of intraparenchymal and intraventricular hemorrhage for clinical catheter puncture path planning. First, we develop a 3D U-Net embedded with a multi-scale boundary aware module and a consistency loss for segmenting two types of hematoma in computed tomography images. The multi-scale boundary aware module can improve the model's ability to understand the two types of hematoma boundaries. The consistency loss can reduce the probability of classifying a pixel into two categories at the same time. Since different hematoma volumes and locations have different treatments. We also measure hematoma volume, estimate centroid deviation, and compare with clinical methods. Finally, we plan the puncture path and conduct clinical validation. We collected a total of 351 cases, and the test set contained 103 cases. For intraparenchymal hematomas, the accuracy can reach 96 % when the proposed method is applied for path planning. For intraventricular hematomas, the proposed model's segmentation efficiency and centroid prediction are superior to other comparable models. Experimental results and clinical practice show that the proposed model has potential for clinical application. In addition, our proposed method has no complicated modules and improves efficiency, with generalization ability.

Pharmacist-led iodinated contrast media infusion risk assessment service.

Background: With the increasing development of medical imaging, the use of iodinated contrast media has become more widespread. Adverse reactions caused by iodinated contrast media have drawn much attention. Despite this, there is still a lack of unified standards for the safe infusion process of iodinated contrast media in clinical practice both domestically and internationally. Objectives: Establishing a risk management service system to better predict the risks associated with iodinated contrast media infusion, reduce the incidence of adverse reactions and minimize patient harm. Method: A prospective interventional study was carried out from April 2021 to December 2021 at Nanjing Drum Tower Hospital in China. During this study, a service system was established to manage the risks associated with the infusion of iodinated contrast media. Personalized risk identification and assessment were performed by a pharmacist-led multidisciplinary team before iodinated contrast media infusion. Early warning, prevention, and adverse reaction management were performed according to different risk levels during and after infusion. Results: A multidisciplinary team led by pharmacists was established to evaluate the risks associated with infusion of iodinated contrast media. A total of 157 patients with risk factors related to the iodinated contrast media were screened out, which prevented 22 serious adverse events and enhanced the quality of medical care. All participants expressed high satisfaction with the service. Conclusion: Through practical exploration, the pharmacist-led multidisciplinary team can provide advance warning and effectively limit the risks of adverse reactions caused by iodinated contrast media to a preventable and controllable level. This approach serves as a valuable reference for developing strategies and schemes to reduce the incidence of such reactions. Therefore, we encourage the implementation of this intervention in other areas of China.

MSU-Net: Multi-scale Sensitive U-Net based on pixel-edge-region level collaborative loss for nasopharyngeal MRI segmentation.

Radiotherapy is the traditional treatment of early nasopharyngeal carcinoma (NPC). Automatic accurate segmentation of risky lesions in the nasopharynx is crucial in radiotherapy. U-Net has been proved its effective medical image segmentation ability. However, the great difference in the structure and size of nasopharynx among different patients requires a network that pays more attention to multi-scale information. In this paper, we propose a multi-scale sensitive U-Net (MSU-Net) based on pixel-edge-region level collaborative loss (LCo-PER) for NPC segmentation task. A series of novel feature fusion modules based on spatial continuity and multi-scale semantic are proposed for extracting multi-level features while efficiently searching for all size lesions. A spatial continuity information extraction module (SCIEM) is proposed for effectively using the spatial continuity information of context slices to search small lesions. And a multi-scale semantic feature extraction module (MSFEM) is proposed for extracting features of different receptive fields. LCo-PER is proposed for the network training which makes network model could take into account the size of different lesions. The global Dice, Precision, Recall and IOU of the testing set are 84.50%, 97.48%, 84.33% and 82.41%, respectively. The results show that our method is better than the other state-of-the-art methods for NPC segmentation which obtain higher accuracy and effective segmentation performance.

Metabolic Anomaly Appearance Aware U-Net for Automatic Lymphoma Segmentation in Whole-Body PET/CT Scans.

Positron emission tomography-computed tomography (PET/CT) is an essential imaging instrument for lymphoma diagnosis and prognosis. PET/CT image based automatic lymphoma segmentation is increasingly used in the clinical community. U-Net-like deep learning methods have been widely used for PET/CT in this task. However, their performance is limited by the lack of sufficient annotated data, due to the existence of tumor heterogeneity. To address this issue, we propose an unsupervised image generation scheme to improve the performance of another independent supervised U-Net for lymphoma segmentation by capturing metabolic anomaly appearance (MAA). Firstly, we propose an anatomical-metabolic consistency generative adversarial network (AMC-GAN) as an auxiliary branch of U-Net. Specifically, AMC-GAN learns normal anatomical and metabolic information representations using co-aligned whole-body PET/CT scans. In the generator of AMC-GAN, we propose a complementary attention block to enhance the feature representation of low-intensity areas. Then, the trained AMC-GAN is used to reconstruct the corresponding pseudo-normal PET scans to capture MAAs. Finally, combined with the original PET/CT images, MAAs are used as the prior information for improving the performance of lymphoma segmentation. Experiments are conducted on a clinical dataset containing 191 normal subjects and 53 patients with lymphomas. The results demonstrate that the anatomical-metabolic consistency representations obtained from unlabeled paired PET/CT scans can be helpful for more accurate lymphoma segmentation, which suggest the potential of our approach to support physician diagnosis in practical clinical applications.

Screening of Early Flowering Lotus (Nelumbo nucifera Gaertn.) Cultivars and Effects of Different Cultivars on Flowering Period.

Flowering time is an important trait that determines the breeding process of ornamental plants. The flowering period of lotus (Nelumbo nucifera Gaertn.) is mainly concentrated in June-August. During this period, the weather is hot and there are few tourists, which made many lotus scenic spots difficult to operate. People have a strong demand for early flowering lotus cultivars. In this paper, 30 lotus cultivars with high ornamental value were selected as materials and their phenological periods were observed for two consecutive years in 2019 and 2020. A number of cultivars with early flowering potential and stable flowering periods, such as 'Fenyanzi', 'Chengshanqiuyue', 'Xianghumingyue' and 'Wuzhilian', were screened by K-Means clustering method. The relationship between accumulated temperature and flowering time of 19 lotus cultivars at different growth stages was analyzed. It was found that lotus cultivars with early flowering traits could adapt well to the changes of early environmental temperature and were not affected by low temperature. On the other hand, by analyzing the relationship between different traits and flowering time of three typical cultivars, such as rhizome weight, phenological period, etc., it shows that the nutrient content of the rhizome and the early morphology of plants will affect the flowering time. These results provide a reference for the formation of a systematic lotus early flowering cultivar breeding mechanism and the establishment of a perfect flowering regulation technology system, which can further improve the ornamental value of lotus and promote industrial development.

Leverage prior texture information in deep learning-based liver tumor segmentation: A plug-and-play Texture-Based Auto Pseudo Label module.

Segmenting the liver and tumor regions using CT scans is crucial for the subsequent treatment in clinical practice and radiotherapy. Recently, liver and tumor segmentation techniques based on U-Net have gained popularity. However, there are numerous varieties of liver tumors, and they differ greatly in terms of their shapes and textures. It is unreasonable to regard all liver tumors as one class for learning. Meanwhile, texture information is crucial for the identification of liver tumors. We propose a plug-and-play Texture-based Auto Pseudo Label (TAPL) module to take use of the texture information of tumors and enable the neural network actively learn the texture differences between various tumors to increase the segmentation accuracy, especially for small tumors. The TPAL module consists of two parts, texture enhancement and texture-based pseudo label generator. To highlight the regions where the texture varies significantly, we enhance the textured areas of the CT image. Based on their texture information, tumors are automatically divided into several classes by the texture-based pseudo label generator. The multi-class tumors produced by the neural network during the prediction step are combined into a single tumor label, which is then used as the outcome of the segmentation. Experiments on clinical dataset and public dataset Lits2017 show that the proposed algorithm outperforms single liver tumor label segmentation methods and is more friendly to small tumors.

A review of deep learning-based multiple-lesion recognition from medical images: classification, detection and segmentation.

Deep learning-based methods have become the dominant methodology in medical image processing with the advancement of deep learning in natural image classification, detection, and segmentation. Deep learning-based approaches have proven to be quite effective in single lesion recognition and segmentation. Multiple-lesion recognition is more difficult than single-lesion recognition due to the little variation between lesions or the too wide range of lesions involved. Several studies have recently explored deep learning-based algorithms to solve the multiple-lesion recognition challenge. This paper includes an in-depth overview and analysis of deep learning-based methods for multiple-lesion recognition developed in recent years, including multiple-lesion recognition in diverse body areas and recognition of whole-body multiple diseases. We discuss the challenges that still persist in the multiple-lesion recognition tasks by critically assessing these efforts. Finally, we outline existing problems and potential future research areas, with the hope that this review will help researchers in developing future approaches that will drive additional advances.

MSRA-Net: Tumor segmentation network based on Multi-scale Residual Attention.

Automatic Medical segmentation of medical images is an important part in the field of computer medical diagnosis, among which tumor segmentation is an important branch of medical image segmentation. Accurate automatic segmentation method is very important in medical diagnosis and treatment. Positron emission computed tomography (PET) and X-ray computed tomography (CT) images are widely used in medical image segmentation to help doctors accurately locate information such as tumor location and shape, providing metabolic and anatomical information, respectively. At present, PET/CT images have not been effectively combined in the research of medical image segmentation, and the complementary semantic information between the superficial and deep layers of neural network has not been ensured. To solve the above problems, this paper proposed a Multi-scale Residual Attention network (MSRA-Net) for tumor segmentation of PET/CT. We first use an attention-fusion based approach to automatically learn the tumor-related areas of PET images and weaken the irrelevant area. Then, the segmentation results of PET branch are processed to optimize the segmentation results of CT branch by using attention mechanism. The proposed neural network (MSRA-Net) can effectively fuse PET image and CT image, which can improve the precision of tumor segmentation by using complementary information of the multi-modal image, and reduce the uncertainty of single modal image segmentation. Proposed model uses multi-scale attention mechanism and residual module, which fuse multi-scale features to form complementary features of different scales. We compare with state-of-the-art medical image segmentation methods. The experiment showed that the Dice coefficient of the proposed network in soft tissue sarcoma and lymphoma datasets increased by 8.5% and 6.1% respectively compared with UNet, showing a significant improvement.

SDA-UNet: a hepatic vein segmentation network based on the spatial distribution and density awareness of blood vessels.

Objective.Hepatic vein segmentation is a fundamental task for liver diagnosis and surgical navigation planning. Unlike other organs, the liver is the only organ with two sets of venous systems. Meanwhile, the segmentation target distribution in the hepatic vein scene is extremely unbalanced. The hepatic veins occupy a small area in abdominal CT slices. The morphology of each person's hepatic vein is different, which also makes segmentation difficult. The purpose of this study is to develop an automated hepatic vein segmentation model that guides clinical diagnosis.Approach.We introduce the 3D spatial distribution and density awareness (SDA) of hepatic veins and propose an automatic segmentation network based on 3D U-Net which includes a multi-axial squeeze and excitation module (MASE) and a distribution correction module (DCM). The MASE restrict the activation area to the area with hepatic veins. The DCM improves the awareness of the sparse spatial distribution of the hepatic veins. To obtain global axial information and spatial information at the same time, we study the effect of different training strategies on hepatic vein segmentation. Our method was evaluated by a public dataset and a private dataset. The Dice coefficient achieves 71.37% and 69.58%, improving 3.60% and 3.30% compared to the other SOTA models, respectively. Furthermore, metrics based on distance and volume also show the superiority of our method.Significance.The proposed method greatly reduced false positive areas and improved the segmentation performance of the hepatic vein in CT images. It will assist doctors in making accurate diagnoses and surgical navigation planning.

MRLA-Net: A tumor segmentation network embedded with a multiple receptive-field lesion attention module in PET-CT images.

The tumor image segmentation is an important basis for doctors to diagnose and formulate treatment planning. PET-CT is an extremely important technology for recognizing the systemic situation of diseases due to the complementary advantages of their respective modal information. However, current PET-CT tumor segmentation methods generally focus on the fusion of PET and CT features. The fusion of features will weaken the characteristics of the modality itself. Therefore, enhancing the modal features of the lesions can obtain optimized feature sets, which is extremely necessary to improve the segmentation results. This paper proposed an attention module that integrates the PET-CT diagnostic visual field and the modality characteristics of the lesion, that is, the multiple receptive-field lesion attention module. This paper made full use of the spatial domain, frequency domain, and channel attention, and proposed a large receptive-field lesion localization module and a small receptive-field lesion enhancement module, which together constitute the multiple receptive-field lesion attention module. In addition, a network embedded with a multiple receptive-field lesion attention module has been proposed for tumor segmentation. This paper conducted experiments on a private liver tumor dataset as well as two publicly available datasets, the soft tissue sarcoma dataset, and the head and neck tumor segmentation dataset. The experimental results showed that the proposed method achieves excellent performance on multiple datasets, and has a significant improvement compared with DenseUNet, and the tumor segmentation results on the above three PET/CT datasets were improved by 7.25%, 6.5%, 5.29% in Dice per case. Compared with the latest PET-CT liver tumor segmentation research, the proposed method improves by 8.32%.

Cytological and Molecular Mechanism of Low Pollen Grain Viability in a Germplasm Line of Double Lotus.

Self-fertilization rate is an essential index of lotus reproductive system development, and pollen activity is a key factor affecting lotus seed setting rate. Based on cytology and molecular biology, this study addresses the main reasons for the low self-set rate of double lotus. It takes two different double lotus breeds into consideration, namely 'Sijingganshan' with a low self-crossing rate and 'Jinfurong' with a high self-crossing rate. Cytological analysis results showed that the pollen abortion caused by excessive degradation of tapetum during the single phase was the root cause for the low self-mating rate of double lotus. Subsequent transcriptome analysis revealed that the gene NnPTC1 related to programmed tapetum cell death was significantly differentially expressed during the critical period of abortion, which further verified the specific expression of NnPTC1 in anthers. It was found that the expression level of NnPTC1 in 'Sijingganshan' at the mononuclear stage of its microspore development was significantly higher than that of 'Jinfurong' at the same stage. The overexpression of NnPTC1 resulted in the premature degradation of the tapetum and significantly decreased seed setting rate. These results indicated that the NnPTC1 gene regulated the pollen abortion of double lotus. The mechanism causing a low seed setting rate for double lotus was preliminarily revealed, which provided a theoretical basis for cultivating lotus varieties with both flower and seed.

The regulatory module MdBZR1-MdCOL6 mediates brassinosteroid- and light-regulated anthocyanin synthesis in apple.

The anthocyanin content is an important indicator of the nutritional value of most fruits, including apple (Malus domestica). Anthocyanin synthesis is coordinately regulated by light and various phytohormones. In this study on apple, we revealed the antagonistic relationship between light and brassinosteroid (BR) signaling pathways, which is mediated by BRASSINAZOLE-RESISTANT 1 (MdBZR1) and the B-box protein MdCOL6. The exogenous application of brassinolide inhibited the high-light-induced anthocyanin accumulation in red-fleshed apple seedlings, whereas increases in the light intensity decreased the endogenous BR content. The overexpression of MdBZR1 inhibited the anthocyanin synthesis in apple plants. An exposure to a high-light intensity induced the degradation of dephosphorylated MdBZR1, resulting in functional impairment. MdBZR1 was identified as an upstream repressor of MdCOL6, which promotes anthocyanin synthesis in apple plants. Furthermore, MdBZR1 interacts with MdCOL6 to attenuate its ability to activate MdUFGT and MdANS transcription. Thus, MdBZR1 negatively regulates MdCOL6-mediated anthocyanin accumulation. Our study findings have clarified the molecular basis of the integration of light and BR signals during the regulation of anthocyanin biosynthesis, which is an important process influencing fruit quality.

CGBO-Net: Cruciform structure guided and boundary-optimized lymphoma segmentation network.

Lymphoma segmentation plays an important role in the diagnosis and treatment of lymphocytic tumor. Most current existing automatic segmentation methods are difficult to give precise tumor boundary and location. Semi-automatic methods are usually combined with manually added features such as bounding box or points to locate the tumor. Inspired by this, we propose a cruciform structure guided and boundary-optimized lymphoma segmentation network(CGBS-Net). The method uses a cruciform structure extracted based on PET images as an additional input to the network, while using a boundary gradient loss function to optimize the boundary of the tumor. Our method is divided into two main stages: In the first stage, we use the proposed axial context-based cruciform structure extraction (CCE) method to extract the cruciform structures of all tumor slices. In the second stage, we use PET/CT and the corresponding cruciform structure as input in the designed network (CGBO-Net) to extract tumor structure and boundary information. The Dice, Precision, Recall, IOU and RVD are 90.7%, 89.4%, 92.5%, 83.1% and 4.5%, respectively. Validate on the lymphoma dataset and publicly available head and neck data, our proposed approach is better than the other state-of-the-art semi-segmentation methods, which produces promising segmentation results.