KRN5b regulates maize kernel row number through mediating phosphoinositol signalling.

Kernel row number (KRN) is a major yield related trait for maize (Zea mays L.) and is also a major goal of breeders, as it can increase the number of kernels per plant. Thus, identifying new genetic factors involving in KRN formation may accelerate improving yield-related traits genetically.

Heterozygous fasciated ear mutations improve yield traits in inbred and hybrid maize lines.

Heterozygous mutations in two genes encoding key regulators of development improve kernel row number in inbred and hybrid maize, revealing their potential for yield improvement.

Coordinated control for the auricle asymmetric development by ZmIDD14 and ZmIDD15 fine-tune the high-density planting adaption in maize.

Multiple distinct specialized regions shape the architecture of maize leaves. Among them, the fringe-like and wedge-shaped auricles alter the angle between the leaf and stalk, which is a key trait in crop plant architecture. As planting density increased, a small leaf angle (LA) was typically selected to promote crop light capture efficiency and yield.

Mapping dynamic spatial patterns of brain function with spatial-wise attention.

: Using functional magnetic resonance imaging (fMRI) and deep learning to discover the spatial pattern of brain function, or functional brain networks (FBNs) has been attracted many reseachers. Most existing works focus on static FBNs or dynamic functional connectivity among fixed spatial network nodes, but ignore the potential dynamic/time-varying characteristics of the spatial networks themselves. And most of works based on the assumption of linearity and independence, that oversimplify the relationship between blood-oxygen level dependence signal changes and the heterogeneity of neuronal activity within voxels.

Spatial-temporal convolutional attention for discovering and characterizing functional brain networks in task fMRI.

Functional brain networks (FBNs) are spatial patterns of brain function that play a critical role in understanding human brain function. There are many proposed methods for mapping the spatial patterns of brain function, however they oversimplify the underlying assumptions of brain function and have various limitations such as linearity and independence. Additionally, current methods fail to account for the dynamic nature of FBNs, which limits their effectiveness in accurately characterizing these networks.

The effect of increasing the gaps between the front teeth on torque and intrusion control of the incisors for anterior retraction with clear aligners: a prospective study.

Objective: To investigate the effect of sequential distalization on increasing gaps in the maxillary anterior teeth, focusing on the control of torque and three-dimensional teeth movement during anterior retraction with clear aligners in extraction cases.

Methods: We recruited 24 patients who were undergoing extraction bilateral maxillary first premolars with clear aligners. According to a predetermined increment in the spaces between the maxillary anterior teeth, the patients were divided into three groups: those with no gap (9 cases), a 0.

Functional brain network identification and fMRI augmentation using a VAE-GAN framework.

Recently, deep learning models have achieved superior performance for mapping functional brain networks from functional magnetic resonance imaging (fMRI) data compared with traditional methods. However, due to the lack of sufficient data and the high dimensionality of brain volume, deep learning models of fMRI tend to suffer from overfitting. In addition, existing methods rarely studied fMRI data augmentation and its application.

End-to-end Prediction of EGFR Mutation Status with Denseformer.

Accurate genotyping of the epidermal growth factor receptor (EGFR) is critical for the treatment planning of lung adenocarcinoma. Currently, clinical identification of EGFR genotyping highly relies on biopsy and sequence testing which is invasive and complicated. Recent advancements in the integration of computed tomography (CT) imagery with deep learning techniques have yielded a non-invasive and straightforward way for identifying EGFR profiles.

A deep learning method for autism spectrum disorder identification based on interactions of hierarchical brain networks.

Background: It has been recently shown that deep learning models exhibited remarkable performance of representing functional Magnetic Resonance Imaging (fMRI) data for the understanding of brain functional activities. With hierarchical structure, deep learning models can infer hierarchical functional brain networks (FBN) from fMRI. However, the applications of the hierarchical FBNs have been rarely studied.

Multi-head attention-based masked sequence model for mapping functional brain networks.

The investigation of functional brain networks (FBNs) using task-based functional magnetic resonance imaging (tfMRI) has gained significant attention in the field of neuroimaging. Despite the availability of several methods for constructing FBNs, including traditional methods like GLM and deep learning methods such as spatiotemporal self-attention mechanism (STAAE), these methods have design and training limitations. Specifically, they do not consider the intrinsic characteristics of fMRI data, such as the possibility that the same signal value at different time points could represent different brain states and meanings.

Accurate corresponding fiber tract segmentation via FiberGeoMap learner with application to autism.

Fiber tract segmentation is a prerequisite for tract-based statistical analysis. Brain fiber streamlines obtained by diffusion magnetic resonance imaging and tractography technology are usually difficult to be leveraged directly, thus need to be segmented into fiber tracts. Previous research mainly consists of two steps: defining and computing the similarity features of fiber streamlines, then adopting machine learning algorithms for fiber clustering or classification.

De novo genome assembly and analyses of 12 founder inbred lines provide insights into maize heterosis.

Hybrid maize displays superior heterosis and contributes over 30% of total worldwide cereal production. However, the molecular mechanisms of heterosis remain obscure. Here we show that structural variants (SVs) between the parental lines have a predominant role underpinning maize heterosis.

ZmBET5L1 inhibits primary root growth and decreases osmotic stress tolerance by mediating vesicle aggregation and tethering in maize.

Improving osmotic stress tolerance is critical to help crops to thrive and maintain high yields in adverse environments. Here, we characterized a core subunit of the transport protein particle (TRAPP) complex, ZmBET5L1, in maize using knowledge-driven data mining and genome editing. We found that ZmBET5L1 can interact with TRAPP I complex subunits and act as a tethering factor to mediate vesicle aggregation and targeting from the endoplasmic reticulum to the Golgi apparatus.

Migration and diffusion of unsteady-state flow of volatile organic compounds on activated carbon adsorption beds under reverse ventilation.

Despite increasing attention to the influence of unsteady-state volatile organic compounds (VOCs) on the adsorption of activated carbon, studies in this regard are rare. Therefore, in this study, an investigation into the migration and diffusion of unsteady-state VOCs on activated carbon adsorption beds under reverse ventilation was conducted. Here, reverse clean air was introduced when the activated carbon bed reached the penetration point.

A NAC-EXPANSIN module enhances maize kernel size by controlling nucellus elimination.

Maize early endosperm development is initiated in coordination with elimination of maternal nucellar tissues. However, the underlying mechanisms are largely unknown. Here, we characterize a major quantitative trait locus for maize kernel size and weight that encodes an EXPANSIN gene, ZmEXPB15.

The Coordinated KNR6-AGAP-ARF1 Complex Modulates Vegetative and Reproductive Traits by Participating in Vesicle Trafficking in Maize.

The ()-mediated phosphorylation of an adenosine diphosphate ribosylation factor (Arf) GTPase-activating protein (AGAP) forms a key regulatory module for the numbers of spikelets and kernels in the ear inflorescences of maize ( L.). However, the action mechanism of the KNR6-AGAP module remains poorly understood.

Modeling and augmenting of fMRI data using deep recurrent variational auto-encoder.

. Recently, deep learning models have been successfully applied in functional magnetic resonance imaging (fMRI) modeling and associated applications. However, there still exist at least two challenges.

Simultaneous spatial-temporal decomposition for connectome-scale brain networks by deep sparse recurrent auto-encoder.

Exploring the spatial patterns and temporal dynamics of human brain activity has been of great interest, in the quest to better understand connectome-scale brain networks. Though modeling spatial and temporal patterns of functional brain networks have been researched for a long time, the development of a unified and simultaneous spatial-temporal model has yet to be realized. For instance, although some deep learning methods have been proposed recently in order to model functional brain networks, most of them can only represent either spatial or temporal perspective of functional Magnetic Resonance Imaging (fMRI) data and rarely model both domains simultaneously.

[Operation and Maintenance of Cost-Effective Volatile Organic Compounds Abatement Alternatives].

Presently, volatile organic compounds (VOCs) pollution control in China has entered the deep-water zone, facing difficult challenges. The cost-effectiveness of VOCs abatement alternatives will determine the final environmental benefits. Screening of abatement alternatives with good cost-effectiveness and performance is important to form a sound basis for VOCs emission abatement work to create sustainable and stable alternatives.