A de novo Gene Promotes Seed Germination Under Drought Stress in Arabidopsis.

The origin of genes from noncoding sequences is a long-term and fundamental biological question. However, how de novo genes originate and integrate into the existing pathways to regulate phenotypic variations is largely unknown. Here, we selected 7 genes from 782 de novo genes for functional exploration based on transcriptional and translational evidence.

Dual structure-aware image filterings for semi-supervised medical image segmentation.

Semi-supervised image segmentation has attracted great attention recently. The key is how to leverage unlabeled images in the training process. Most methods maintain consistent predictions of the unlabeled images under variations (e.

WASPSYN: A Challenge for Domain Adaptive Synapse Detection in Microwasp Brain Connectomes.

The size of image volumes in connectomics studies now reaches terabyte and often petabyte scales with a great diversity of appearance due to different sample preparation procedures. However, manual annotation of neuronal structures (e.g.

Molecular mechanisms of adaptive evolution in wild animals and plants.

Wild animals and plants have developed a variety of adaptive traits driven by adaptive evolution, an important strategy for species survival and persistence. Uncovering the molecular mechanisms of adaptive evolution is the key to understanding species diversification, phenotypic convergence, and inter-species interaction. As the genome sequences of more and more non-model organisms are becoming available, the focus of studies on molecular mechanisms of adaptive evolution has shifted from the candidate gene method to genetic mapping based on genome-wide scanning.

Polishing Performance and Removal Mechanism of Core-Shell Structured Diamond/SiO Abrasives on Sapphire Wafer.

Corrosive and toxic solutions are normally employed to polish sapphire wafers, which easily cause environmental pollution. Applying green polishing techniques to obtain an ultrasmooth sapphire surface that is scratch-free and has low damage at high polishing efficiency is a great challenge. In this paper, novel diamond/SiO composite abrasives were successfully synthesized by a simplified sol-gel strategy.

Deep learning-assisted diagnosis of parotid gland tumors by using contrast-enhanced CT imaging.

Objectives: Imaging interpretation of the benignancy or malignancy of parotid gland tumors (PGTs) is a critical consideration prior to surgery in view of therapeutic and prognostic values of such discrimination. This study investigates the application of a deep learning-based method for preoperative stratification of PGTs.

Materials And Methods: Using the 3D DenseNet-121 architecture and a dataset consisting of 117 volumetric arterial-phase contrast-enhanced CT scans, we developed a binary classifier for PGT distinction and tested it.

Arabidopsis EXECUTER1 interacts with WRKY transcription factors to mediate plastid-to-nucleus singlet oxygen signaling.

Chloroplasts produce singlet oxygen (1O2), which causes changes in nuclear gene expression through plastid-to-nucleus retrograde signaling to increase plant fitness. However, the identity of this 1O2-triggered pathway remains unclear. Here, we identify mutations in GENOMES UNCOUPLED4 (GUN4) and GUN5 as suppressors of phytochrome-interacting factor1 (pif1) pif3 in regulating the photo-oxidative response in Arabidopsis thaliana.

Scaling-up and proteomic analysis reveals photosynthetic and metabolic insights toward prolonged H photoproduction in Chlamydomonas hpm91 mutant lacking proton gradient regulation 5 (PGR5).

Clean and sustainable H production is crucial to a carbon-neutral world. H generation by Chlamydomonas reinhardtii is an attractive approach for solar-H from HO. However, it is currently not large-scalable because of lacking desirable strains with both optimal H productivity and sufficient knowledge of underlying molecular mechanism.

Local Intensity Order Transformation for Robust Curvilinear Object Segmentation.

Segmentation of curvilinear structures is important in many applications, such as retinal blood vessel segmentation for early detection of vessel diseases and pavement crack segmentation for road condition evaluation and maintenance. Currently, deep learning-based methods have achieved impressive performance on these tasks. Yet, most of them mainly focus on finding powerful deep architectures but ignore capturing the inherent curvilinear structure feature (e.

Erratum: Author Correction: Advancing COVID-19 diagnosis with privacy-preserving collaboration in artificial intelligence.

[This corrects the article DOI: 10.1038/s42256-021-00421-z.].

Advancing COVID-19 diagnosis with privacy-preserving collaboration in artificial intelligence.

Artificial intelligence provides a promising solution for streamlining COVID-19 diagnoses; however, concerns surrounding security and trustworthiness impede the collection of large-scale representative medical data, posing a considerable challenge for training a well-generalized model in clinical practices. To address this, we launch the Unified CT-COVID AI Diagnostic Initiative (UCADI), where the artificial intelligence (AI) model can be distributedly trained and independently executed at each host institution under a federated learning framework without data sharing. Here we show that our federated learning framework model considerably outperformed all of the local models (with a test sensitivity/specificity of 0.

Advancing COVID-19 Diagnosis with Privacy-Preserving Collaboration in Artificial Intelligence.

Artificial intelligence (AI) provides a promising substitution for streamlining COVID-19 diagnoses. However, concerns surrounding security and trustworthiness impede the collection of large-scale representative medical data, posing a considerable challenge for training a well-generalised model in clinical practices. To address this, we launch the Unified CT-COVID AI Diagnostic Initiative (UCADI), where the AI model can be distributedly trained and independently executed at each host institution under a federated learning framework (FL) without data sharing.

Cell Localization and Counting Using Direction Field Map.

Automatic cell counting in pathology images is challenging due to blurred boundaries, low-contrast, and overlapping between cells. In this paper, we train a convolutional neural network (CNN) to predict a two-dimensional direction field map and then use it to localize cell individuals for counting. Specifically, we define a direction field on each pixel in the cell regions (obtained by dilating the original annotation in terms of cell centers) as a two-dimensional unit vector pointing from the pixel to its corresponding cell center.

Less Is More, Natural Loss-of-Function Mutation Is a Strategy for Adaptation.

Gene gain and loss are crucial factors that shape the evolutionary success of diverse organisms. In the past two decades, more attention has been paid to the significance of gene gain through gene duplication or genes. However, gene loss through natural loss-of-function (LoF) mutations, which is prevalent in the genomes of diverse organisms, has been largely ignored.

Automated brain MRI metrics in the EPIRMEX cohort of preterm newborns: Correlation with the neurodevelopmental outcome at 2 years.

Purpose: The purpose of this study was to identify in the EPIRMEX cohort the correlations between MRI brain metrics, including diffuse excessive high signal intensities (DEHSI) obtained with an automated quantitative method and neurodevelopmental outcomes at 2 years.

Materials And Methods: A total of 390 very preterm infants (gestational age at birth≤32 weeks) who underwent brain MRI at term equivalent age at 1.5T (n=338) or 3T (n=52) were prospectively included.

Deep Learning for Detecting Cerebral Aneurysms with CT Angiography.

Background Cerebral aneurysm detection is a challenging task. Deep learning may become a supportive tool for more accurate interpretation. Purpose To develop a highly sensitive deep learning-based algorithm that assists in the detection of cerebral aneurysms on CT angiography images.

Affinity Space Adaptation for Semantic Segmentation Across Domains.

Semantic segmentation with dense pixel-wise annotation has achieved excellent performance thanks to deep learning. However, the generalization of semantic segmentation in the wild remains challenging. In this paper, we address the problem of unsupervised domain adaptation (UDA) in semantic segmentation.

A collaborative online AI engine for CT-based COVID-19 diagnosis.

Artificial intelligence can potentially provide a substantial role in streamlining chest computed tomography (CT) diagnosis of COVID-19 patients. However, several critical hurdles have impeded the development of robust AI model, which include deficiency, isolation, and heterogeneity of CT data generated from diverse institutions. These bring about lack of generalization of AI model and therefore prevent it from applications in clinical practices.

Transcriptional repression specifies the central cell for double fertilization.

Double fertilization is a key innovation for the evolutionary success of angiosperms by which the two fertilized female gametes, the egg cell and central cell, generate the embryo and endosperm, respectively. The female gametophyte (embryo sac) enclosed in the sporophyte is derived from a one-celled haploid cell lineage. It undergoes successive events of mitotic divisions, cellularization, and cell specification to give rise to the mature embryo sac, which contains the two female gametes accompanied by two types of accessory cells, namely synergids and antipodals.

Gliding Vertex on the Horizontal Bounding Box for Multi-Oriented Object Detection.

Object detection has recently experienced substantial progress. Yet, the widely adopted horizontal bounding box representation is not appropriate for ubiquitous oriented objects such as objects in aerial images and scene texts. In this paper, we propose a simple yet effective framework to detect multi-oriented objects.