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. Compared to traffic noise and white noise, drivers' cognitive efficiency was lower under entertainment noise. (3) Performance in complex cognitive tasks was more susceptible to noise levels compared to simple cognitive tasks; (4) Experimentally, it was found that drivers exhibited the highest cognitive efficiency in cognitive tasks when the environmental noise level was 55 dB(A), as opposed to noise levels of 35 dB(A), 45 dB(A), and 65 dB(A).

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.). A total of 60 people took part in both experiments and performed two tasks: maximisation and discrimination. The experiments revealed that the addition of attributes, such as colour, marker or linear, decreased response time (RT), but the combination of colour & linear & marker increased RT. This is more significant in discrimination tasks. We provide empirical evidence for the design of time-series data visualisations and encourage the combination of two of these graphical attributes, such as colour & linear, colour & marker or linear & marker, when conditions allow, to improve decision-making efficiency.

Few scholars have studied the impact of graphical attributes on decision-making efficiency in data visualisation. This study explores the effect of graphical attributes with different redundancy levels on decision-making efficiency through behavioural experiments. It has been found that moderately redundant graphical attributes in difficult tasks can significantly improve decision-making efficiency.

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. Comparisons against 19 state-of-the-art methods using both simulated and real ST datasets from various tissues and ST technologies demonstrate that SPACEL outperforms the others for cell type deconvolution, for spatial domain identification, and for 3D alignment, thus showcasing SPACEL as a valuable integrated toolkit for ST data processing and analysis.

Genome-wide association analysis of hyperspectral reflectance data to dissect the genetic architecture of growth-related traits in maize under plant growth-promoting bacteria inoculation.

Plant growth-promoting bacteria (PGPB) may be of use for increasing crop yield and plant resilience to biotic and abiotic stressors. Using hyperspectral reflectance data to assess growth-related traits may shed light on the underlying genetics as such data can help assess biochemical and physiological traits. This study aimed to integrate hyperspectral reflectance data with genome-wide association analyses to examine maize growth-related traits under PGPB inoculation. A total of 360 inbred maize lines with 13,826 single nucleotide polymorphisms (SNPs) were evaluated with and without PGPB inoculation; 150 hyperspectral wavelength reflectances at 386-1021 nm and 131 hyperspectral indices were used in the analysis. Plant height, stalk diameter, and shoot dry mass were measured manually. Overall, hyperspectral signatures produced similar or higher genomic heritability estimates than those of manually measured phenotypes, and they were genetically correlated with manually measured phenotypes. Furthermore, several hyperspectral reflectance values and spectral indices were identified by genome-wide association analysis as potential markers for growth-related traits under PGPB inoculation. Eight SNPs were detected, which were commonly associated with manually measured and hyperspectral phenotypes. Different genomic regions were found for plant growth and hyperspectral phenotypes between with and without PGPB inoculation. Moreover, the hyperspectral phenotypes were associated with genes previously reported as candidates for nitrogen uptake efficiency, tolerance to abiotic stressors, and kernel size. In addition, a Shiny web application was developed to explore multiphenotype genome-wide association results interactively. Taken together, our results demonstrate the usefulness of hyperspectral-based phenotyping for studying maize growth-related traits in response to PGPB inoculation.

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. A behavioral experiment was adopted, and the average reaction time of emergency braking was used as the evaluation index of driver performance. RESULTS: Both ambient temperature and vehicle speed had a statistically significant relationship with driver's braking reaction time, and the interaction effect was significant. CONCLUSION: Drivers' cognitive efforts in medium-temperature environments were significantly lower than that at high and low temperatures. Compared with previous studies, this study also monitored differences in the activity of drivers' brain regions in three ambient temperatures, providing a physiological basis for measuring drivers' cognitive efforts.

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. These parameters are derived from bio-optical models based on the Google Earth Engine (GEE) cloud computing platform and time series of high-resolution Sentinel-2 images. Our proposed method effectively addresses the misclassification issue between the aquaculture ponds and rivers, lakes, reservoirs, and salt pans and achieves an overall accuracy of 91 % and a Kappa coefficient of 0.83 in the Chinese coastal zone. Our results indicate that the total area of Chinese coastal aquaculture ponds was 1,039,214 ha in 2019, mainly distributed in the Shandong and Guangdong provinces. The highest aquaculture intensity occurs within the 1 km coastal buffer zone, accounting for 22.4 % of the total area. Furthermore, more than half of the Chinese coastal aquaculture ponds are concentrated in the 0-5 km buffer zone. Our method is of general applicability and thus is suitable for large-scale aquaculture ponds mapping projects. Moreover, the biophysical parameters we employ can be considered as new indicators for the classification of various water bodies even with different aquaculture species.

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. Nevertheless, the performance of the CV systems is sensitive to the quality of imagery features. When the CV system is deployed in a variable environment, its performance may decrease as the features are not generalized enough under different illumination conditions. Moreover, most CV systems are established by supervised learning, in which intensive effort in labeling ground truths for the training process is required. Hence, a semi-supervised pipeline, VTag, is developed in this study. The pipeline focuses on long-term tracking of pig activity without requesting any pre-labeled video but a few human supervisions to build a CV system. The pipeline can be rapidly deployed as only one top-view RGB camera is needed for the tracking task. Additionally, the pipeline was released as a software tool with a friendly graphical interface available to general users. Among the presented datasets, the average tracking error was 17.99 cm. Besides, with the prediction results, the pig moving distance per unit time can be estimated for activity studies. Finally, as the motion is monitored, a heat map showing spatial hot spots visited by the pigs can be useful guidance for farming management. The presented pipeline saves massive laborious work in preparing training dataset. The rapid deployment of the tracking system paves the way for pig behavior monitoring.

Collecting detailed measurements of animals through cameras has become an important focus with the rising demand for meat production in the swine industry. Currently, researchers use computational approaches to train models to recognize pig morphological features and monitor pig behaviors automatically. Though little human effort is needed after model training, current solutions require a large amount of pre-selected images for the training process, and the expensive preparation work is difficult for many farms to implement such practice. Hence, a pipeline, VTag, is presented to address these challenges in our study. With few supervisions, VTag can automatically track positions of multiple pigs from one single top-view RGB camera. No pre-labeled images are required to establish a robust pig tracking system. Additionally, the pipeline was released as a software tool with a friendly graphical user interface, that is easy to learn for general users. Among the presented datasets, the average tracking error is 17.99 cm, which is shorter than one-third of the pig body length in the study. The estimated pig activity from VTag can serve as useful farming guidance. The presented strategy saves massive laborious work in preparing training datasets and setting up monitoring environments. The rapid deployment of the tracking system paves the way for pig behavior monitoring.

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. In this article, we present XSim Version 2 that is an open-source tool and has been extensively redesigned with additional features to meet the needs in modern breeding programs. It seamlessly incorporates multiple statistical models for genetic evaluations, such as GBLUP, Bayesian alphabets, and neural networks, and it can effortlessly simulate successive generations of descendants based on complex mating schemes by the aid of its modular design. Case studies are presented to demonstrate the flexibility of XSim Version 2 in simulating crossbreeding in animal and plant populations. Modern biotechnology, including double haploids and embryo transfer, can all be simultaneously integrated into the mating plans that drive the simulation. From a computing perspective, XSim Version 2 is implemented in Julia, which is a computer language that retains the readability of scripting languages (e.g. R and Python) without sacrificing much computational speed compared to compiled languages (e.g. C). This makes XSim Version 2 a simulation tool that is relatively easy for both champions and community members to maintain, modify, or extend in order to improve their breeding programs. Functions and operators are overloaded for a better user interface so they may concatenate, subset, summarize, and organize simulated populations at each breeding step. With the strong and foreseeable demands in the community, XSim Version 2 will serve as a modern simulator bridging the gaps between theories and experiments with its flexibility, extensibility, and friendly interface.

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. In this study, a method employed a process-based hydrodynamic model and remote sensing (RS) was developed to evaluate the impacts of EEP on the geomorphological change of a prototype Ramsar site. Results demonstrated that RS can improve the quality of bathymetry data for the numerical model with a decrease of RMSE of bathymetry data from 0.52 m to 0.3 m. RS data also showed good capacity in trend detection of geomorphological change spatially. Results showed the Chongming Dongtan wetland experienced erosion with an annual rate of -0.035 m/yr from 2013 to 2016 after the implementation of EEP. The deposition rate changed significantly in the area within 200 m of the EEP. It is found that the EEP modified the composition of vegetation, sediment transportation, as well as substrate stability, affecting the geomorphological change of coastal wetlands. The study suggested that the EEP is a direct and effective way to restore the coastal habitats for waterbirds from moderate anthropogenic disturbance. However, the modification of the coastal wetland ecosystem by EEP will potentially increase the vulnerability to global climate change. Therefore, Future studies are needed to further evaluate the advantages and disadvantages of EEP and identify a more sustainable approach for coastal management.

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. The results showed that in 2018, there were 1136.56 km2 of coastal wetlands along the coast of Fujian with an overall accuracy of 95.63%, which were mainly distributed in estuaries and bays. These coastal wetlands consisted of tidal flats, low marshes, and high marshes with proportions of 84.91%, 13.05%, and 2.04%, respectively. An unprecedented loss of coastal wetlands has occurred in Fujian Province, with an annual rate of 15.44 km2/a from 1994 to 2018. Many coastal wetlands were reclaimed, dredged, and converted into inland areas for aquaculture ponds, ports, and built-up areas in different urbanization periods, which has led to a great loss of coastal spaces with an area of 476.87 km2. The interplay between the loss of coastal wetlands and seaward urbanization will lead to severe fragmentation and squeezing effects in the coastal zone and will weaken the coastal protection from marine disasters that is provided by coastal wetlands. Therefore, we conceived two conceptional frameworks for sustainable coastal protection based on the current situations of the coastal communities to provide a trade-off between economic development and the protection of coastal developing countries in the world.

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. One of the major bottlenecks is the labor-intensive phenotyping burden for biomass selection. In this study, we employed two alfalfa fields to pave a path to overcome the challenge by using UAV images with fully automatic field plot segmentation for high-throughput phenotyping. The first field was used to develop the prediction model and the second field to validate the predictions. The first and second fields had 808 and 1025 plots, respectively. The first field had three harvests with biomass measured in May, July, and September of 2019. The second had one harvest with biomass measured in September of 2019. These two fields were imaged one day before harvesting with a DJI Phantom 4 pro UAV carrying an additional Sentera multispectral camera. Alfalfa plot images were extracted by GRID software to quantify vegetative area based on the Normalized Difference Vegetation Index. The prediction model developed from the first field explained 50-70% (R Square) of biomass variation in the second field by incorporating four features from UAV images: vegetative area, plant height, Normalized Green-Red Difference Index, and Normalized Difference Red Edge Index. This result suggests that UAV-based, high-throughput phenotyping could be used to improve the efficiency of the biomass selection process in alfalfa breeding programs.

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. These traits included milk yield, fat, and protein. We used the estimated breeding values as dependent variables to conduct the genome-wide association studies (GWAS). Breeding values were estimated through pedigree relationships by using a linear mixed model. Genotyping was carried out on the individuals with phenotypes by using the Illumina BovineSNP150 BeadChip. The association analyses were conducted by using the fixed and random model Circulating Probability Unification (FarmCPU) method. A total of ten single-nucleotide polymorphisms (SNPs) were detected above the genome-wide significant threshold (p < 4.0 × 10-7), including six located in previously reported quantitative traits locus (QTL) regions. We found eight candidate genes within distances of 120 kb upstream or downstream to the associated SNPs. The study not only identified the effect of DGAT1 gene on milk fat and protein, but also discovered novel genetic loci and candidate genes related to milk traits. These novel genetic loci would be an important basis for molecular breeding in dairy cattle.

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. Phenotypic data were collected for 2410 individuals measured during 1995-2017. By adding another 445 ancestors, a total of 2855 related individuals were used to derive estimated breeding values for all individuals, including the 2410 individuals with phenotypes. Among phenotyped individuals, we genotyped 403 cows with the Illumina 150 K Bovine BeadChip. RESULTS: GWAS were conducted with the FarmCPU (Fixed and random model circulating probability unification) method. We identified 12 markers significantly associated with six of the 10 traits under the threshold of 5% after a Bonferroni multiple test correction. Seven of these SNPs were in QTL regions previously identified to be associated with related traits. One identified SNP, BovineHD1600006691, was significantly associated with both age at first service and age at first calving. This SNP directly overlapped a QTL previously reported to be associated with calving ease. Within 160 Kb upstream and downstream of each significant SNP identified, we speculated candidate genes based on functionality. Four of the SNPs were located within four candidate genes, including CDH2, which is linked to milk fat percentage, and GABRG2, which is associated with milk protein yield. CONCLUSIONS: These findings are beneficial not only for breeding through marker-assisted selection, but also for genome editing underlying the related traits to enhance the overall performance of dual-purpose cattle.

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. Furthermore, the majority of these tools are executed through a command-line interface (CLI), which requires programming skills. Non-programmers struggle to use them efficiently because of the steep learning curves and zero tolerance for data formats and mistakes when inputting keywords and parameters. To address these problems, this study developed a software package, named the Intelligent Prediction and Association Tool (iPat), with a user-friendly graphical user interface. With iPat, GWAS or GS can be performed using a pointing device to simply drag and/or click on graphical elements to specify input data files, choose input parameters and select analytical models. Models available to users include those implemented in third party CLI packages such as GAPIT, PLINK, FarmCPU, BLINK, rrBLUP and BGLR. Users can choose any data format and conduct analyses with any of these packages. File conversions are automatically conducted for specified input data and selected packages. A GWAS-assisted genomic prediction method was implemented to perform genomic prediction using any GWAS method such as FarmCPU. iPat was written in Java for adaptation to multiple operating systems including Windows, Mac and Linux. Availability and implementation: The iPat executable file, user manual, tutorials and example datasets are freely available at http://zzlab.net/iPat. Contact: zhiwu.zhang@wsu.edu.