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Multi-scale image decomposition is crucial for image fusion, extracting prominent feature textures from infrared and visible light images to obtain clear fused images with more textures. This paper proposes a fusion method of infrared and visible light images based on spatial domain and image features to obtain high-resolution and texture-rich images. First, an efficient hierarchical image clustering algorithm based on superpixel fast pixel clustering directly performs multi-scale decomposition of each source image in the spatial domain and obtains high-frequency, medium-frequency, and low-frequency layers to extract the maximum and minimum values of each source image combined images. Then, using the attribute parameters of each layer as fusion weights, high-definition fusion images are through adaptive feature fusion. Besides, the proposed algorithm performs multi-scale decomposition of the image in the spatial frequency domain to solve the information loss problem caused by the conversion process between the spatial frequency and frequency domains in the traditional extraction of image features in the frequency domain. Eight image quality indicators are compared with other fusion algorithms. Experimental results show that this method outperforms other comparative methods in both subjective and objective measures. Furthermore, the algorithm has high definition and rich textures.

Landslide disasters frequently occur along the highway G30 in the Guozigou Valley, the corridor of energy, material, economic and cultural exchange, etc., between Yili and other cities of China and Central Asia. However, little attention has been paid to assess the detailed landslide susceptibility of the strategically important highway, especially with high spatial resolution data and the generative presence-only MaxEnt model. Landslide susceptibility assessment (LSA) is a first and vital step for preventing and mitigating landslide hazards. The goal of the current study was to perform LSA for the landslide-prone highway G30 in Guozigou Valley, China with the aid of GIS tools and Chinese high resolution Gaofen-1 (GF-1) satellite data, and analyze and compare the performance of the maximum entropy (MaxEnt) model and logistic regression (LR). Thirty five landslides were determined in the study region, using GF-1 satellite data, official data, and field surveys. Seven landslide conditioning factors, including altitude, slope, aspect, gully density, lithology, faults density, and NDVI, were used to investigate their existing spatial relationships with landslide occurrences. The LR and MaxEnt model performance were assessed by the receiver operating characteristic curve, presenting areas under the curve equal to 0.85 and 0.94, respectively. The performance of the MaxEnt model was slightly better than that of the LR model. A landslide susceptibility map was created through reclassifying the landslides occurrence probability with the classification method of natural breaks. According to the MaxEnt model results, 3.29% and 3.82% of the study region is highly and very highly susceptible to future landslide events, respectively, with the highest landslide susceptibility along the highway. The generated landslide susceptibility map could help government agencies and decision-makers to make wise decisions for preventing or mitigating landslide hazards along the highway and design schemes of highway engineering and maintenance in Guozigou Valley, the mountainous areas.

Semantic Change Detection (SCD) aims to accurately identify the change areas and their categories in dual-time images, which is more complex and challenging than traditional binary change detection tasks. Accurately capturing the change information of land cover types is crucial for remote sensing image analysis and subsequent decision-making applications. However, existing SCD methods often neglect the spatial details and temporal dependencies of dual-time images, leading to problems such as change category imbalance and limited detection accuracy, especially in capturing small target changes. To address this issue, this study proposes a network that guides multitask semantic change detection through spatiotemporal semantic interaction (STGNet). STGNet enhances the ability to capture spatial details by introducing a Detail-Aware Path (DAP) and designs a Bidirectional Guidance Module for Spatial Detail and Semantic Information for adaptive feature selection, improving feature extraction capabilities in complex scenes. Furthermore, to resolve the inconsistency between semantic information and change areas, this paper designs a Cross-Temporal Refinement Interaction Module (CTIM), which enables cross-time scale feature fusion and interaction, constraining the consistency of detection results and improving the recognition accuracy of unchanged areas. To further enhance detection performance, a dynamic depthwise separable convolution is designed in the CTIM module, which can adaptively adjust convolution kernels to more precisely capture change features in different regions of the image. Experimental results on three SCD datasets show that the proposed method outperforms other existing methods in various evaluation metrics. In particular, on the Landsat-SCD dataset, the F1 score (F1 scd ) reaches 91.64%, and the separation Kappa coefficient improves by 17.68%. These experimental results fully demonstrate the significant advantages of STGNet in improving semantic change detection accuracy, robustness, and generalization capability.

Background Expressed Sequence Tags (ESTs) have played significant roles in gene discovery and gene functional analysis, especially for non-model organisms. For organisms with no full genome sequences available, ESTs are normally assembled into longer consensus sequences for further downstream analysis. However current de novo EST assembly programs often generate large number of assembly errors that will negatively affect the downstream analysis. In order to generate more accurate consensus sequences from ESTs, tools are needed to reduce or eliminate errors from de novo assemblies. Results We present iAssembler, a pipeline that can assemble large-scale ESTs into consensus sequences with significantly higher accuracy than current existing assemblers. iAssembler employs MIRA and CAP3 assemblers to generate initial assemblies, followed by identifying and correcting two common types of transcriptome assembly errors: 1) ESTs from different transcripts (mainly alternatively spliced transcripts or paralogs) are incorrectly assembled into same contigs; and 2) ESTs from same transcripts fail to be assembled together. iAssembler can be used to assemble ESTs generated using the traditional Sanger method and/or the Roche-454 massive parallel pyrosequencing technology. Conclusion We compared performances of iAssembler and several other de novo EST assembly programs using both Roche-454 and Sanger EST datasets. It demonstrated that iAssembler generated significantly more accurate consensus sequences than other assembly programs.

It is unclear whether there are individual differences in the long-term efficacy of computer-assisted and traditional total knee arthroplasty. The purpose of this study was to perform a meta-analysis comparing the same individuals undergoing computer-assisted and traditional total knee arthroplasty separately to determine whether computer-assisted total knee arthroplasty can provide better lower extremity radiographic results and clinical outcomes. We searched literatures to identify relevant randomized controlled trials comparing the effects of computer-assisted and traditional methods in bilateral total knee arthroplasty. After screening, quality evaluation and data extraction according to inclusion and exclusion criteria, the quality and bias risks of the included studies were evaluated. The meta-analysis compared the radiographic results, functional outcomes and complications of the two techniques. Six clinical controlled trials were included, with total of 1098 patients. The meta-analysis showed that the accuracy in terms of the mechanical axis of the lower extremity, the sagittal alignment of the femoral component and the coronal alignment of the tibial component in computer-assisted total knee arthroplasty was significantly better than those in traditional total knee arthroplasty. There were no differences in the functional results, revision rates or aseptic loosening rates between the two techniques. After excluding individual differences such as bone development and bone quality, although computer-assisted techniques can better accurately correct the mechanical axis of the lower extremity and the position of prosthesis implantation than traditional techniques, there is no significant difference in the functional results and revision rate of bilateral total knee arthroplasty in the same individual.

Gaseous phytohormone ethylene regulates various aspects of plant development. Ethylene is perceived by ER membrane-localized receptors, which are inactivated upon binding with ethylene molecules, thereby initiating ethylene signal transduction. Here, we report that a novel E3 ligase RING finger for Ethylene receptor Degradation (RED) and its E2 partner UBC32 ubiquitinate ethylene-bound receptors for degradation through an ER associated degradation (ERAD) pathway in both Rosa hybrida and Solanum lycopersicum . The depletion of RED or UBC32 leads to hypersensitivity to ethylene, which is manifested as premature leaf abscission and petal shedding in roses, as well as the dwarf plants and accelerated fruit ripening in tomatoes. Disruption of the conserved ethylene binding site of receptors prevents RED-mediated degradation of the receptors. Our study discovers an ERAD branch that facilitates the ethylene-induced degradation of receptors, and provides insights into how the plant’s response to ethylene can be controlled by modulating the turnover of ethylene receptors. Zhao et al. identified a novel E3 ligase RED and its E2 partner UBC32 which mediates ethylene-induced degradation of ethylene receptors ETR3 via the ERAD pathway in Rosa hybrida and Solanum lycopersicum , providing new insights into controlling ethylene response through receptor turnover.

The Qinghai–Tibet Plateau is a crucial ecological security barrier in China and Asia. Its grassland ecosystem has high ecological service value. Scientific assessments and classifications of grasslands are crucial for determining the value of grassland resources and implementing refined management. Traditional grassland classification methods have used expert knowledge and linear models, which are subjective and cannot describe complex nonlinear relationships. We conducted a case study in Hongyuan County, Sichuan Province, in the water conservation area of the Qinghai–Tibet Plateau, using multi-source data including Landsat 8 (15 m/30 m), MOD15A2 (500 m), ALOS imagery (12.5 m), and 435 field survey samples, combined with machine learning models such as convolutional neural network (CNN), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), histogram gradient boosting (HistGradientBoosting), and random forest (RF). The objective was to develop a novel grassland classification method that integrates multi-source remote sensing data with machine learning algorithms. Based on the evaluation metrics of SHAP values, mean annual precipitation (MAP, 0.675), >0 °C Accumulated Temperature (AT, 0.591), and aspect (ASPECT, 0.548) were the most critical factors influencing alpine grasslands, revealing a driving mechanism characterized by climate dominance, topographic regulation, soil support, and vegetation response. The XGBoost model demonstrated the best performance (with an accuracy of 0.829, Precision of 0.818, Recall of 0.829, weighted F1-score of 0.820, and an AUC value of 0.870). The pixel-by-pixel absolute difference calculation between the model-predicted and the actual classification results showed that regions with no discrepancy (absolute value = 0) accounted for 75.82%, those with a minor discrepancy (absolute value = 1) accounted for 23.63%, and regions with a major discrepancy (absolute value = 2) accounted for only 0.54%. This study has established a replicable paradigm for the precise management and conservation of alpine grassland resources. Through the synergistic application of deep learning and machine learning, it generated superior baseline data, quantitatively uncovered a grassland differentiation mechanism dominated by hydrothermal factors and fine-tuned by topography in the complex Qinghai–Tibet Plateau, and delivered high-precision spatial distribution maps of grassland classes.

Previous studies of highly branched mutants in pea (rms1–rms5), Arabidopsis thaliana (max1–max4), petunia (dad1–dad3), and rice (d3, d10, htd1/d17, d14, d27) identified strigolactones or their derivates (SLs), as shoot branching inhibitors. This recent discovery offers the possibility of using SLs to regulate branching commercially, for example, in chrysanthemum, an important cut flower crop. To investigate this option, SL physiology and molecular biology were studied in chrysanthemum (Dendranthema grandiflorum), focusing on the CCD8/MAX4/DAD1/RMS1/D10 gene. Our results suggest that, as has been proposed for Arabidopsis, the ability of SLs to inhibit bud activity depends on the presence of a competing auxin source. The chrysanthemum SL biosynthesis gene, CCD8 was cloned, and found to be regulated in a similar, but not identical way to known CCD8s. Expression analyses revealed that DgCCD8 is predominantly expressed in roots and stems, and is up-regulated by exogenous auxin. Exogenous SL can down-regulate DgCCD8 expression, but this effect can be overridden by apical auxin application. This study provides evidence that SLs are promising candidates to alter the shoot branching habit of chrysanthemum.

The need for high-affinity, SARS-CoV-2-specific monoclonal antibodies (mAbs) is critical in the face of the global COVID-19 pandemic, as such reagents can have important diagnostic, research, and therapeutic applications. Of greatest interest is the ~ 300 amino acid receptor binding domain (RBD) within the S1 subunit of the spike protein because of its key interaction with the human angiotensin converting enzyme 2 (hACE2) receptor present on many cell types, especially lung epithelial cells. We report here the development and functional characterization of 29 nM-affinity mouse SARS-CoV-2 mAbs created by an accelerated immunization and hybridoma screening process. Differing functions, including binding of diverse protein epitopes, viral neutralization, impact on RBD-hACE2 binding, and immunohistochemical staining of infected lung tissue, were correlated with variable gene usage and sequence.

The diverse plasticity of plant architecture is largely determined by shoot branching. Shoot branching is an event regulated by multiple environmental, developmental and hormonal stimuli through triggering lateral bud response. After perceiving these signals, the lateral buds will respond and make a decision on whether to grow out. TCP transcriptional factors, BRC1/TB1/FC1, were previously proven to be involved in local inhibition of shoot branching in Arabidopsis, pea, tomato, maize and rice. To investigate the function of BRC1, we isolated the BRC1 homolog from chrysanthemum. There were two transcripts of DgBRC1 coming from two alleles in one locus, both of which complemented the multiple branches phenotype of Arabidopsis brc1-1, indicating that both are functionally conserved. DgBRC1 was mainly expressed in dormant axillary buds, and down-regulated at the bud activation stage, and up-regulated by higher planting densities. DgBRC1 transcripts could respond to apical auxin supply and polar auxin transport. Moreover, we found that the acropetal cytokinin stream promoted branch outgrowth whether or not apical auxin was present. Basipetal cytokinin promoted outgrowth of branches in the absence of apical auxin, while strengthening the inhibitory effects on lower buds in the presence of apical auxin. The influence of auxin and strigolactons (SLs) on the production of cytokinin was investigated, we found that auxin locally down-regulated biosynthesis of cytokinin in nodes, SLs also down-regulated the biosynthesis of cytokinin, the interactions among these phytohormones need further investigation.