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ObjectivesThis study investigated the usefulness and performance of a two-stage attention-aware convolutional neural network (CNN) for the automated diagnosis of otitis media from tympanic membrane (TM) images.DesignA classification model development and validation study in ears with otitis media based on otoscopic TM images. Two commonly used CNNs were trained and evaluated on the dataset. On the basis of a Class Activation Map (CAM), a two-stage classification pipeline was developed to improve accuracy and reliability, and simulate an expert reading the TM images.Setting and participantsThis is a retrospective study using otoendoscopic images obtained from the Department of Otorhinolaryngology in China. A dataset was generated with 6066 otoscopic images from 2022 participants comprising four kinds of TM images, that is, normal eardrum, otitis media with effusion (OME) and two stages of chronic suppurative otitis media (CSOM).ResultsThe proposed method achieved an overall accuracy of 93.4% using ResNet50 as the backbone network in a threefold cross-validation. The F1 Score of classification for normal images was 94.3%, and 96.8% for OME. There was a small difference between the active and inactive status of CSOM, achieving 91.7% and 82.4% F1 scores, respectively. The results demonstrate a classification performance equivalent to the diagnosis level of an associate professor in otolaryngology.ConclusionsCNNs provide a useful and effective tool for the automated classification of TM images. In addition, having a weakly supervised method such as CAM can help the network focus on discriminative parts of the image and improve performance with a relatively small database. This two-stage method is beneficial to improve the accuracy of diagnosis of otitis media for junior otolaryngologists and physicians in other disciplines.

Background This study aimed to develop and validate a deep learning (DL) model to identify atelectasis and attic retraction pocket in cases of otitis media with effusion (OME) using multi-center otoscopic images. Method A total of 6393 OME otoscopic images from three centers were used to develop and validate a DL model for detecting atelectasis and attic retraction pocket. A threefold random cross-validation procedure was adopted to divide the dataset into training validation sets on a patient level. A team of otologists was assigned to diagnose and characterize atelectasis and attic retraction pocket in otoscopic images. Receiver operating characteristic (ROC) curves, including area under the ROC curve (AUC), accuracy, sensitivity, and specificity were used to assess the performance of the DL model. Class Activation Mapping (CAM) illustrated the discriminative regions in the otoscopic images. Results Among all OME otoscopic images, 3564 (55.74%) were identified with attic retraction pocket, and 2460 (38.48%) with atelectasis. The diagnostic DL model of attic retraction pocket and atelectasis achieved a threefold cross-validation accuracy of 89% and 79%, AUC of 0.89 and 0.87, a sensitivity of 0.93 and 0.71, and a specificity of 0.62 and 0.84, respectively. Larger and deeper cases of atelectasis and attic retraction pocket showed greater weight, based on the red color depicted in the heat map of CAM. Conclusion The DL algorithm could be employed to identify atelectasis and attic retraction pocket in otoscopic images of OME, and as a tool to assist in the accurate diagnosis of OME.

As a key component of terrestrial ecosystems, soil interacts directly with aboveground vegetation. Evaluating soil quality is therefore of great significance to comprehensively explore the interaction mechanism of this association. The purpose of this study was to fully understand the characteristics of aboveground vegetation, soil quality, and their potential coupling relationship among different forest types in Hunan Province, and to provide a theoretical basis for further exploring the mechanisms underlying soil–vegetation interactions in central China. We have set up sample plots of five kinds of forests (namely broad-leaved forest, coniferous forest, coniferous broad-leaved mixed forest, bamboo forest, and shrub forest) in Hunan Province. To explore the differences of vegetation characteristics and soil physical and chemical properties among the five stand types, variance analysis, principal component analysis, and regression analysis were used. Finally, we explored the coupling relationship between soil quality and aboveground vegetation characteristics of each forest. We found that there were significant differences in soil quality among the forest types, ranked as follows: shrub forest > bamboo forest > broad-leaved forest > mixed coniferous and broad-leaved forest > coniferous forest. In general, there was a negative correlation between vegetation richness and soil quality in the broad-leaved forest and the shrub forest, but they showed a positive correlation in the coniferous forest, the mixed coniferous and broad-leaved forest, and the bamboo forest. As a necessary habitat condition for aboveground vegetation, soil directly determines the survival and prosperity of plant species. These results indicated that for vegetation–soil dynamics in a strong competitive environment, as one aspect wanes the other waxes. However, in a weak competitive environment, the adverse relationship between vegetation and soil is less pronounced and their aspects can promote.

Global Local Climate Zone (LCZ) maps, indicating urban structures and land use, are crucial for Urban Heat Island (UHI) studies and also as starting points to better understand the spatio-temporal dynamics of cities worldwide. However, reliable LCZ maps are not available on a global scale, hindering scientific progress across a range of disciplines that study the functionality of sustainable cities. As a first step towards large-scale LCZ mapping, this paper tries to provide guidance about data/feature choice. To this end, we evaluate the spectral reflectance and spectral indices of the globally available Sentinel-2 and Landsat-8 imagery, as well as the Global Urban Footprint (GUF) dataset, the OpenStreetMap layers buildings and land use and the Visible Infrared Imager Radiometer Suite (VIIRS)-based Nighttime Light (NTL) data, regarding their relevance for discriminating different Local Climate Zones (LCZs). Using a Residual convolutional neural Network (ResNet), a systematic analysis of feature importance is performed with a manually-labeled dataset containing nine cities located in Europe. Based on the investigation of the data and feature choice, we propose a framework to fully exploit the available datasets. The results show that GUF, OSM and NTL can contribute to the classification accuracy of some LCZs with relatively few samples, and it is suggested that Landsat-8 and Sentinel-2 spectral reflectances should be jointly used, for example in a majority voting manner, as proven by the improvement from the proposed framework, for large-scale LCZ mapping.

Moso bamboo ( Phyllostachys edulis ) is an economically and ecologically important nontimber forestry species. Further development of this species as a sustainable bamboo resource has been hindered by a lack of population genome information. Here, we report a moso bamboo genomic variation atlas of 5.45 million single-nucleotide polymorphisms (SNPs) from whole-genome resequencing of 427 individuals covering 15 representative geographic areas. We uncover low genetic diversity, high genotype heterozygosity, and genes under balancing selection underlying moso bamboo population adaptation. We infer its demographic history with one bottleneck and its recently small population without a rebound. We define five phylogenetic groups and infer that one group probably originated by a single-origin event from East China. Finally, we conduct genome-wide association analysis of nine important property-related traits to identify candidate genes, many of which are involved in cell wall, carbohydrate metabolism, and environmental adaptation. These results provide a foundation and resources for understanding moso bamboo evolution and the genetic mechanisms of agriculturally important traits. Moso bamboo is an economically and ecologically important nontimber forestry species. Here, the authors analyze 427 genomes collected from 15 representative geographic areas, and identify genes under balancing selection, putative patterns of historic demography, and candidate genes associated with important traits.

Human operators have the trend of increasing physical and mental workloads when performing teleoperation tasks in uncertain and dynamic environments. In addition, their performances are influenced by subjective factors, potentially leading to operational errors or task failure. Although agent-based methods offer a promising solution to the above problems, the human experience and intelligence are necessary for teleoperation scenarios. In this paper, a truncated quantile critics reinforcement learning-based integrated framework is proposed for human–agent teleoperation that encompasses training, assessment and agent-based arbitration. The proposed framework allows for an expert training agent, a bilateral training and cooperation process to realize the co-optimization of agent and human. It can provide efficient and quantifiable training feedback. Experiments have been conducted to train subjects with the developed algorithm. The performances of human–human and human–agent cooperation modes are also compared. The results have shown that subjects can complete the tasks of reaching and picking and placing with the assistance of an agent in a shorter operational time, with a higher success rate and less workload than human–human cooperation.

Urbanization is a substantial contributor to anthropogenic environmental change, and often occurs at a rapid pace that demands frequent and accurate monitoring. Time series of satellite imagery collected at fine spatial resolution using stable spectral bands over decades are most desirable for this purpose. In practice, however, temporal spectral variance arising from variations in atmospheric conditions, sensor calibration, cloud cover, and other factors complicates extraction of consistent information on changes in urban land cover. Moreover, the construction and application of effective training samples is time-consuming, especially at continental and global scales. Here, we propose a new framework for satellite-based mapping of urban areas based on transfer learning and deep learning techniques. We apply this method to Landsat observations collected during 1984–2016 and extract annual records of urban areas in four cities in the temperate zone (Beijing, New York, Melbourne, and Munich). The method is trained using observations of Beijing collected in 1999, and then used to map urban areas in all target cities for the entire 1984–2016 period. The method addresses two central challenges in long term detection of urban change: temporal spectral variance and a scarcity of training samples. First, we use a recurrent neural network to minimize seasonal urban spectral variance. Second, we introduce an automated transfer strategy to maximize information gain from limited training samples when applied to new target cities in similar climate zones. Compared with other state-of-the-art methods, our method achieved comparable or even better accuracy: the average change detection accuracy during 1984–2016 is 89% for Beijing, 94% for New York, 93% for Melbourne, and 89% for Munich, and the overall accuracy of single-year urban maps is approximately 96 ± 3% among the four target cities. The results demonstrate the practical potential and suitability of the proposed framework. The method is a promising tool for detecting urban change in massive remote sensing data sets with limited training data.

Diet regulates the gut microbiota, which in turn affects animal performance, but how diet shapes the animal performance and gut microbiota remains largely unknown. To fill this gap, the author conducted a comprehensive study of the influence of total mixed ration (TMR) or fermented TMR (FTMR) on the animal performance and gut microbiome. Sixteen Simmental male cattle were randomly allocated to two treatments (one cattle per pen). The animals were fed with the TMR and FTMR diets respectively. The results showed that the contents of ADF, NDF, cellulose and total cellulose in the FTMR were significantly decreased ( p  < 0.05), the average daily weight gain of the Simmental male cattle shows an increasing trend (TMR: 0.31 vs. FTMR: 0.62), while no significant ( p  = 0.2382) difference was found between the two treatments. The metagenomics analysis showed significant ( p  < 0.05) difference in the α-diversity and β-diversity, and the dominant bacterial genera were Weissella , Lactiplantibacillus , Levilactobacillus and Companilactobacillus . The 16S rRNA sequencing indicated that a significant ( p  = 0.018) difference in the bacterial communities between the cattle fed with TMR or FTMR diet, while no significant ( p  < 0.05) differences were detected on the primary genus. It can be found that the FTMR diet increased the average daily gain of cattle by improving the chemical composition and microbial functional profile of the FTMR diet, and affected the growth performance of cattle.

China's edible oil self-sufficiency rate is only about 30%, and approximately 100 million acres of saline-alkali land are potentially available for rapeseed cultivation. However, salt stress severely inhibits rapeseed growth and yield, making the breeding of salt-tolerant varieties and the study of underlying molecular mechanisms urgent priorities. In this study, 1609 accessions were screened for salt tolerance, and the highly salt-tolerant material 'Xiangnong Saline-Alkali Oil No. 1' (XNSA01) was identified. Subsequent physiological, hormonal, transcriptomic, and functional analyses were conducted to characterize its salt-tolerance mechanisms. Salt stress significantly activated antioxidant defense and stress-related hormone responses in XNSA01, accompanied by significant accumulation of proline, abscisic acid, and salicylic acid. Transcriptome analysis identified five candidate salt-responsive genes, among which , , and were closely associated with antioxidant enzyme activities and endogenous hormone contents. In particular, showed a significant negative correlation with MDA content. Yeast heterologous expression and transgenic validation further demonstrated that enhanced salt tolerance by increasing antioxidant enzyme activities and reducing membrane lipid peroxidation. These findings provide a useful basis for the breeding of salt-tolerant rapeseed varieties and for further investigation of salt-tolerance mechanisms in .

This study aimed to evaluate the effects of cellulase or Lactobacillus plantarum ( L. plantarum ) on the fermentation characteristics and microbial community structure of the sorghum straw silage. Sorghum straw was treated with the following four experimental conditions: distilled water (control, CK), cellulase (CEL), Lactobacillus plantarum (LP), and a combined treatment of Lactobacillus plantarum with cellulase (LPCEL). These results indicated that the LP treatment could markedly ( p  < 0.05) preserve the crude protein content compared to that in other treatments, whereas the CEL significantly ( p  < 0.05) reduced the acid detergent fiber content, while the LPCEL had the highest lactic acid content and lowest pH value. Proteobacteria and Pantoea were identified as the dominant phylum and genus in fresh materials, respectively. This phylum level dominance transitioned to Firmicutes post-treatment, while at the genus level, the community shifted from Pantoea to co-dominance of Lactobacillus and Prevotella , with Lactobacillus being the most abundant in both the CEL and LPCEL treatments. In conclusion, adding L. plantarum and cellulase to sorghum straw can significantly improve the fermentation quality of sorghum straw silage, and improve the nutritional value of silage by affecting the microbial community structure and metabolic pathways. Graphical Abstract Highlights • The physicochemical properties of sorghum straw silage were improved by L. plantarum or cellulase. • Microbiomic analyses revealed a progressive reduction after ensiling progress. • The ensiling parameters were influenced by the microbial communities and functions.