Genetic characterization and fine mapping of a recessive genic male-sterile gene in flowering Chinese cabbage (Brassica rapa var. parachinensis).

Brassica vegetables exhibit pronounced heterosis; nevertheless, investigations on fertility-related genes are scarce. The present study scrutinized a recessive genic male-sterile line, 7-3A, capable of generating a completely sterile population, holding significant promise for flowering Chinese cabbage breeding. By whole-genome resequencing of sterile and fertile plants, the male-sterile gene was confined to approximately 185 kb on chromosome A07, situated between markers C719 and NP10 in Brassica rapa var. Chiifu-401. Notably, substantial structural variation was identified within this region across diverse Brassica rapa reference genomes. Despite discernible expression level disparities of a homologous gene, Bnams4b, between male sterile and fertile plants, no sequence divergence was detected. Further elucidation is required to pinpoint a novel sterile gene within the candidate interval. This investigation contributes to the advancement of both the molecular-assisted breeding scheme for flowering Chinese cabbage and the comprehension of male sterility mechanisms. Supplementary Information: The online version contains supplementary material available at 10.1007/s13205-024-04005-7.

DRAC 2022: A public benchmark for diabetic retinopathy analysis on ultra-wide optical coherence tomography angiography images.

We described a challenge named "DRAC - Diabetic Retinopathy Analysis Challenge" in conjunction with the 25th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2022). Within this challenge, we provided the DRAC datset, an ultra-wide optical coherence tomography angiography (UW-OCTA) dataset (1,103 images), addressing three primary clinical tasks: diabetic retinopathy (DR) lesion segmentation, image quality assessment, and DR grading. The scientific community responded positively to the challenge, with 11, 12, and 13 teams submitting different solutions for these three tasks, respectively. This paper presents a concise summary and analysis of the top-performing solutions and results across all challenge tasks. These solutions could provide practical guidance for developing accurate classification and segmentation models for image quality assessment and DR diagnosis using UW-OCTA images, potentially improving the diagnostic capabilities of healthcare professionals. The dataset has been released to support the development of computer-aided diagnostic systems for DR evaluation.

A deep learning system for predicting time to progression of diabetic retinopathy.

Diabetic retinopathy (DR) is the leading cause of preventable blindness worldwide. The risk of DR progression is highly variable among different individuals, making it difficult to predict risk and personalize screening intervals. We developed and validated a deep learning system (DeepDR Plus) to predict time to DR progression within 5 years solely from fundus images. First, we used 717,308 fundus images from 179,327 participants with diabetes to pretrain the system. Subsequently, we trained and validated the system with a multiethnic dataset comprising 118,868 images from 29,868 participants with diabetes. For predicting time to DR progression, the system achieved concordance indexes of 0.754-0.846 and integrated Brier scores of 0.153-0.241 for all times up to 5 years. Furthermore, we validated the system in real-world cohorts of participants with diabetes. The integration with clinical workflow could potentially extend the mean screening interval from 12 months to 31.97 months, and the percentage of participants recommended to be screened at 1-5 years was 30.62%, 20.00%, 19.63%, 11.85% and 17.89%, respectively, while delayed detection of progression to vision-threatening DR was 0.18%. Altogether, the DeepDR Plus system could predict individualized risk and time to DR progression over 5 years, potentially allowing personalized screening intervals.

An overview of artificial intelligence in diabetic retinopathy and other ocular diseases.

Artificial intelligence (AI), also known as machine intelligence, is a branch of science that empowers machines using human intelligence. AI refers to the technology of rendering human intelligence through computer programs. From healthcare to the precise prevention, diagnosis, and management of diseases, AI is progressing rapidly in various interdisciplinary fields, including ophthalmology. Ophthalmology is at the forefront of AI in medicine because the diagnosis of ocular diseases heavy reliance on imaging. Recently, deep learning-based AI screening and prediction models have been applied to the most common visual impairment and blindness diseases, including glaucoma, cataract, age-related macular degeneration (ARMD), and diabetic retinopathy (DR). The success of AI in medicine is primarily attributed to the development of deep learning algorithms, which are computational models composed of multiple layers of simulated neurons. These models can learn the representations of data at multiple levels of abstraction. The Inception-v3 algorithm and transfer learning concept have been applied in DR and ARMD to reuse fundus image features learned from natural images (non-medical images) to train an AI system with a fraction of the commonly used training data (<1%). The trained AI system achieved performance comparable to that of human experts in classifying ARMD and diabetic macular edema on optical coherence tomography images. In this study, we highlight the fundamental concepts of AI and its application in these four major ocular diseases and further discuss the current challenges, as well as the prospects in ophthalmology.

Stretchable 3D lattice conductors.

3D architectures have been long harnessed to create lightweight yet strong cellular materials; however, the study regarding how 3D architectures facilitate the design of soft materials is at the incipient stage. Here, we demonstrate that 3D architectures can greatly facilitate the design of an intrinsically stretchable lattice conductor. We show that 3D architectures can be harnessed to enhance the overall stretchability of the soft conductors, reduce the effective density, enable resistive sensing of the large deformation of curved solids, and improve monitoring of a wastewater stream. Theoretical models are developed to understand the mechanical and conductive behaviors of the lattice conductor. We expect this type of lattice conductors can potentially inspire various designs of 3D-architected electronics for diverse applications from healthcare devices to soft robotics.