Anomalous scaling of branching tidal networks in global coastal wetlands and mudflats.

Branching networks are key elements in natural landscapes and have attracted sustained research interest across the geosciences and numerous intersecting fields. The prevailing consensus has long held that branching networks are optimized and exhibit fractal properties adhering to power-law scaling relationships. However, tidal networks in coastal wetlands and mudflats exhibit scaling properties that defy conventional power-law descriptions, presenting a longstanding enigma.

Mutation of rice SM1 enhances solid leaf midrib formation and increases methane emissions.

The leaf midrib system is essential for plant growth and development, facilitating nutrient transport, providing structural support, enabling gas exchange, and enhancing resilience to environmental stresses. However, the molecular mechanism regulating leaf midrib development is still unclear.In this study, we reported a rice solid midrib 1 (sm1) mutant, exhibiting solid leaf aerenchyma and abaxial rolling leaves due to abnormal development of parenchyma and bulliform cells.

Mutation of KAP, which encodes a keratin-associated protein, affects grain size and yield production in rice.

Grain size and shape are critical agronomic traits that directly impact rice grain yield. Identifying genes that control these traits can provide new strategies for yield improvement. In this study, we characterized a rice mutant, reduced grain length (rgl), which exhibited decreased grain length due to reduced cell proliferation.

Do redundant graphical attributes reduce decision-making efficiency?

Multiple time-series graphs are commonly used for data visualisation, but few scholars have investigated the impact of graphical attributes on decision-making efficiency. This study explores the effects of graphical attributes of varying redundancy conditions on decision-making efficiency. Two experimental conditions were developed for the experiment: non-redundant (independent graphical attributes: colour, linear and marker) and redundant (combinations of two and more graphical attributes: colour and linear, colour and marker, etc.

The impact of environmental noise on drivers' cognitive abilities: A case study on in-vehicle voice interaction interfaces.

To investigate the impact of environmental noise on the cognitive abilities of drivers, this study, using in-vehicle voice interaction as an example, conducted laboratory experiments to assess the effects of road traffic noise, entertainment noise, and white noise stimuli on drivers' attention and short-term memory. The noise levels simulated to mimic acoustic conditions during car driving ranged from 35 dB(A) to 65 dB(A). The conclusions drawn were as follows: (1) Noise levels directly influenced subjective annoyance levels, with annoyance linearly increasing as noise levels escalated; (2) Both attention and short-term memory task reaction times of drivers were significantly influenced by noise types.

SPACEL: deep learning-based characterization of spatial transcriptome architectures.

Spatial transcriptomics (ST) technologies detect mRNA expression in single cells/spots while preserving their two-dimensional (2D) spatial coordinates, allowing researchers to study the spatial distribution of the transcriptome in tissues; however, joint analysis of multiple ST slices and aligning them to construct a three-dimensional (3D) stack of the tissue still remain a challenge. Here, we introduce spatial architecture characterization by deep learning (SPACEL) for ST data analysis. SPACEL comprises three modules: Spoint embeds a multiple-layer perceptron with a probabilistic model to deconvolute cell type composition for each spot in a single ST slice; Splane employs a graph convolutional network approach and an adversarial learning algorithm to identify spatial domains that are transcriptomically and spatially coherent across multiple ST slices; and Scube automatically transforms the spatial coordinate systems of consecutive slices and stacks them together to construct a 3D architecture of the tissue.

How do different ambient temperatures and vehicle speeds affect the cognitive performance of male drivers? Evidence from ERP.

Objective: Excessive cold or overheating can cause a decline in driver performance and even serious traffic accidents, but the influence mechanism of ambient temperature and vehicle speeds on drivers' cognitive performance is still unclear. This research developed an easy driving simulator experiment to study driver performance under different ambient temperatures and vehicle speeds.

Method: Simulated driving tasks were performed by 30 male participants at different speeds in low, medium, and high-temperature environments.

Accurate mapping of Chinese coastal aquaculture ponds using biophysical parameters based on Sentinel-2 time series images.

Aquaculture plays a crucial role in the global food security and nutrition supply, where China accounts for the largest market share. Although there are some studies that focus on large-scale extraction of coastal aquaculture ponds from satellite images, they have often variable accuracies and encounter misclassification due to the similar geometric characteristics of various vivid water bodies. This paper proposes an efficient and novel method that integrates the spatial characteristics and three biophysical parameters (Chlorophyll-a, Trophic State Index, and Floating Algae Index) to map coastal aquaculture ponds at a national scale.

VTag: a semi-supervised pipeline for tracking pig activity with a single top-view camera.

Precision livestock farming has become an important research focus with the rising demand of meat production in the swine industry. Currently, the farming practice is widely conducted by the technology of computer vision (CV), which automates monitoring pig activity solely based on video recordings. Automation is fulfilled by deriving imagery features that can guide CV systems to recognize animals' body contours, positions, and behavioral categories.

XSim version 2: simulation of modern breeding programs.

Simulation can be an efficient approach to design, evaluate, and optimize breeding programs. In the era of modern agriculture, breeding programs can benefit from a simulator that integrates various sources of big data and accommodates state-of-the-art statistical models. The initial release of XSim, in which stochastic descendants can be efficiently simulated with a drop-down strategy, has mainly been used to validate genomic selection results.

Increasing fragmentation and squeezing of coastal wetlands: Status, drivers, and sustainable protection from the perspective of remote sensing.

Coastal wetlands are of great ecological and economic value but face significant degradation and losses because of human activities. Nevertheless, the changes in spatiotemporal landscape patterns, which have occurred as a result of coastal wetland losses, have not been well documented under the rapid urbanization in coastal zones. In this study, an algorithm based on periodic tidal inundations and full time-series indices was developed to map the detailed status and trends in the coastal wetlands in Fujian Province from 1994 to 2018 by using more than archived 5000 Landsat images.

Tracking the environmental impacts of ecological engineering on coastal wetlands with numerical modeling and remote sensing.

Coastal wetlands are the most valuable ecosystems on the earth but facing severe degradation and losses owing to climate change and anthropogenic activities. Many ecological engineering projects (EEP) have been conducted to mitigate the degradation of coastal wetlands. However, the geomorphological impacts of EEP on coastal wetlands have not been well documented.

Validation of UAV-based alfalfa biomass predictability using photogrammetry with fully automatic plot segmentation.

Alfalfa is the most widely cultivated forage legume, with approximately 30 million hectares planted worldwide. Genetic improvements in alfalfa have been highly successful in developing cultivars with exceptional winter hardiness and disease resistance traits. However, genetic improvements have been limited for complex economically important traits such as biomass.

GWAS-Based Identification of New Loci for Milk Yield, Fat, and Protein in Holstein Cattle.

High-yield and high-quality of milk are the primary goals of dairy production. Understanding the genetic architecture underlying these milk-related traits is beneficial so that genetic variants can be targeted toward the genetic improvement. In this study, we measured five milk production and quality traits in Holstein cattle population from China.

Genome-wide association study of milk and reproductive traits in dual-purpose Xinjiang Brown cattle.

Background: Dual-purpose cattle are more adaptive to environmental challenges than single-purpose dairy or beef cattle. Balance among milk, reproductive, and mastitis resistance traits in breeding programs is therefore more critical for dual-purpose cattle to increase net income and maintain well-being. With dual-purpose Xinjiang Brown cattle adapted to the Xinjiang Region in northwestern China, we conducted genome-wide association studies (GWAS) to dissect the genetic architecture related to milk, reproductive, and mastitis resistance traits.

iPat: intelligent prediction and association tool for genomic research.

Summary: The ultimate goal of genomic research is to effectively predict phenotypes from genotypes so that medical management can improve human health and molecular breeding can increase agricultural production. Genomic prediction or selection (GS) plays a complementary role to genome-wide association studies (GWAS), which is the primary method to identify genes underlying phenotypes. Unfortunately, most computing tools cannot perform data analyses for both GWAS and GS.