Enhances the resistance of rice to lepidopteran pests by fusing the Cry1Ca and Cry2Aa genes with self-cleavage peptide sequence.

Background: Accumulation of two or more Bacillus thuringiensis (Bt) proteins in plant not only improves the resistance to pests and broadens the resistance spectrum of crops, but also delays the development of pest resistance.

Results: The self-cleavage peptide sequence was used to link two codon-optimized genes, so as to achieve simultaneous accumulation of two low homologous insecticidal proteins in one plant. The rice transformants accumulating Cry1Ca and Cry2Aa proteins were fed to local lepidopteran pests and the larva mortality in 5 days were 100%.

Association between anxiety, depression, and symptom burden in patients with advanced colorectal cancer: A multicenter cross-sectional study.

Objectives: Patients with advanced colorectal cancer (CRC) have multiple concurrent physical and psychological symptoms. This study aimed to explore the relationship between anxiety, depression, and symptom burden in advanced CRC.

Methods: A multicenter cross-sectional study was conducted in 10 cancer centers from geographically and economically diverse sites in China.

Physical symptoms and anxiety and depression in older patients with advanced cancer in China: a network analysis.

Background: Little is understood about the association between psychosomatic symptoms and advanced cancer among older Chinese patients.

Methods: This secondary analysis was part of a multicenter cross-sectional study based on an electronic patient-reported outcome platform. Patients with advanced cancer were included between August 2019 and December 2020 in China.

Cell wall-localized Bt protein endows rice high resistance to Lepidoptera pests.

Background: The commercialized Bt (Bacillus thuringiensis) crops accumulate Bt protein within cells, but the intracellular interactions of foreign protein with endogenous protein inevitably result in large or small unintended effects. In this study, the Bt gene Cry1Ca was linked with the sequences of extracellular secretion signal peptide and carbohydrate binding module 11 to constitute a fusion gene SP-Cry1Ca-CBM11, and the fusion gene driven by constitutive promoters was used for secreting and anchoring onto the cell wall to minimize unintended effects.

Results: The transient expression in tobacco leaves demonstrated that the fusion protein was anchored on cell walls.

DTAN: Diffusion-based Text Attention Network for medical image segmentation.

In the current era, diffusion models have emerged as a groundbreaking force in the realm of medical image segmentation. Against this backdrop, we introduce the Diffusion Text-Attention Network (DTAN), a pioneering segmentation framework that amalgamates the principles of text attention with diffusion models to enhance the precision and integrity of medical image segmentation. Our proposed DTAN architecture is designed to steer the segmentation process towards areas of interest by leveraging a text attention mechanism.

TGDAUNet: Transformer and GCNN based dual-branch attention UNet for medical image segmentation.

Accurate and automatic segmentation of medical images is a key step in clinical diagnosis and analysis. Currently, the successful application of Transformers' model in the field of computer vision, researchers have begun to gradually explore the application of Transformers in medical segmentation of images, especially in combination with convolutional neural networks with coding-decoding structure, which have achieved remarkable results in the field of medical segmentation. However, most studies have combined Transformers with CNNs at a single scale or processed only the highest-level semantic feature information, ignoring the rich location information in the lower-level semantic feature information.

TransCUNet: UNet cross fused transformer for medical image segmentation.

Accurate segmentation of medical images is crucial for clinical diagnosis and evaluation. However, medical images have complex shapes, the structures of different objects are very different, and most medical datasets are small in scale, making it difficult to train effectively. These problems increase the difficulty of automatic segmentation.

DEAU-Net: Attention networks based on dual encoder for Medical Image Segmentation.

In recent years, variant networks derived from U-Net networks have achieved better results in the field of medical image segmentation. However, we found during our experiments that the current mainstream networks still have certain shortcomings in the learning and extraction of detailed features. Therefore, in this paper, we propose a feature attention network based on dual encoder.

Phenotypic and genetic dissection of the contents of important metallic elements in hybrid rice grown in cadmium-contaminated paddy fields.

Rice ( L.) is a staple food that feeds over half of the world's population, and the contents of metallic elements in rice grain play important roles in human nutrition. In this study, the contents of important metallic elements were determined by ICP-OES, and included cadmium (Cd), zinc (Zn), manganese (Mn), copper (Cu), iron (Fe), nickel (Ni), calcium (Ca), and magnesium (Mg) in brown rice, in the first node from the top (Node 1), in the second node from the top (Node 2), and in roots of 55 hybrids and their parental lines.

Non-same-scale feature attention network based on BPD for medical image segmentation.

The accuracy of diagnosis in medical systems requires automatic image segmentation techniques to provide accurate segmented images of lesions. Segmented images need to be more accurate not only in terms of shape size but also in terms of position. In recent years, a large number of deep learning algorithms have worked tirelessly on this goal.

Knockdown of ZNF280A inhibits cell proliferation and promotes cell apoptosis of bladder cancer.

Objective: ZNF280A is a member of the zinc finger protein family, whose role in human cancers is little known and rarely reported. This study aimed to investigate the role of ZNF280A in bladder cancer.

Methods: Immunohistochemical analysis was performed to detect the expression of ZNF280A in clinical samples.

Transformer with progressive sampling for medical cellular image segmentation.

The convolutional neural network, as the backbone network for medical image segmentation, has shown good performance in the past years. However, its drawbacks cannot be ignored, namely, convolutional neural networks focus on local regions and are difficult to model global contextual information. For this reason, transformer, which is used for text processing, was introduced into the field of medical segmentation, and thanks to its expertise in modelling global relationships, the accuracy of medical segmentation was further improved.

Transformer-based progressive residual network for single image dehazing.

Introduction: The seriously degraded fogging image affects the further visual tasks. How to obtain a fog-free image is not only challenging, but also important in computer vision. Recently, the vision transformer (ViT) architecture has achieved very efficient performance in several vision areas.

MPSHT: Multiple Progressive Sampling Hybrid Model Multi-Organ Segmentation.

Background: In recent years, computer-assisted diagnosis of patients is an increasingly common topic. Multi-organ segmentation of clinical Computed Tomography (CT) images of the patient's abdomen and magnetic resonance images (MRI) of the patient's heart is a challenging task in medical image segmentation. The accurate segmentation of multiple organs is an important prerequisite for disease diagnosis and treatment planning.

DBMF: Dual Branch Multiscale Feature Fusion Network for polyp segmentation.

Accurate and reliable segmentation of colorectal polyps is important for the diagnosis and treatment of colorectal cancer. Most of the existing polyp segmentation methods innovatively combine CNN with Transformer. Due to the single combination approach, there are limitations in establishing connections between local feature information and utilizing global contextual information captured by Transformer.

Changes in Patient-Reported Health Status in Advanced Cancer Patients from a Symptom Management Clinic: A Longitudinal Study Conducted in China.

Objectives: The integration of patient-reported health status has been increasingly emphasised for delivering high-quality care to advanced cancer patients. This research is designed to track health status changes over time in Chinese advanced cancer patients to explore the risk factors affecting their health status.

Methods: Advanced cancer patients were recruited from Peking University Cancer Hospital.

LAD-Net: A Novel Light Weight Model for Early Apple Leaf Pests and Diseases Classification.

Aphids, brown spots, mosaics, rusts, powdery mildew and Alternaria blotches are common types of early apple leaf pests and diseases that severely affect the yield and quality of apples. Recently, deep learning has been regarded as the best classification model for apple leaf pests and diseases. However, these models with large parameters have difficulty providing an accurate and fast diagnosis of apple leaf pests and diseases on mobile terminals.

Implementing Symptom Management Follow-up Using an Electronic Patient-Reported Outcome Platform in Outpatients With Advanced Cancer: Longitudinal Single-Center Prospective Study.

Background: Patients with cancer experience multiple symptoms related to cancer, cancer treatment, and the procedures involved in cancer care; however, many patients with pain, depression, and fatigue, especially those outside the hospital, receive inadequate treatment for their symptoms. Using an electronic patient-reported outcome (ePRO) platform to conduct symptom management follow-up in outpatients with advanced cancer could be a novel and potentially effective approach. However, empirical evidence describing in detail the preparation and implementation courses in a real setting is needed.

Attention based multi-scale parallel network for polyp segmentation.

Colonoscopy is an effective method for detecting colorectal polyps and preventing colorectal cancer. Therefore, in clinical practice, it is very important to accurately segment the location and shape of polyps from colorectal images, which can effectively assist clinicians in their diagnosis. However, the varying sizes and shapes of colorectal polyps and the fact that the polyps to be segmented are very small and closely resemble their surroundings make this a challenging task.

Low-Light Image Enhancement Network Based on Recursive Network.

In low-light environments, image acquisition devices do not obtain sufficient light sources, resulting in low brightness and contrast of images, which poses a great obstacle for other computer vision tasks to be performed. To enable other vision tasks to be performed smoothly, it is essential to enhance the research on low-light image enhancement algorithms. In this article, a multi-scale feature fusion image enhancement network based on recursive structure is proposed.

Prevalence of DSM-5 somatic symptom disorder in Chinese patients with breast cancer.

Objective: The aims of this study were to explore the frequency of somatic symptom disorder (SSD) and the relationship between SSD and somatic, psychological, and social factors in Chinese patients with breast cancer.

Methods: This multicenter cross-sectional study enrolled 264 patients with breast cancer from three different departments in Beijing. The structured clinical interview for fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (SCID-5) for SSD.

Attention-Guided Multi-Scale Feature Fusion Network for Low-Light Image Enhancement.

Low-light image enhancement has been an important research branch in the field of computer vision. Low-light images are characterized by poor visibility, high noise and low contrast. To improve low-light images generated in low-light environments and night conditions, we propose an Attention-Guided Multi-scale feature fusion network (MSFFNet) for low-light image enhancement for enhancing the contrast and brightness of low-light images.