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Abstract This study aimed to construct a novel pelvis-prostate model BPPP which consists of body mass index (BMI), prostate volume (PV), pelvic cavity index (PCI) and prostate-muscle index (PMI) to predict the immediate urinary continence after Retzius-sparing robot assisted laparoscopic radical prostatectomy (RS-RARP). The perioperative data of patients with prostate cancer who underwent RS-RARP in the department of urology of Nanjing Drum Tower Hospital from June 2018 to June 2022 were retrospectively analyzed. 280 patients were eligible for this study in total. Multivariate analysis showed that BMI, PV, PCI, PMI and NVB preservation were significantly associated with immediate urinary continence after RS-RARP. Subgroup analysis showed that patients with low BMI, low PV, high PCI and high PMI had a higher recovery rate of immediate urinary continence. The area under the curve of BPPP (BMI + PV + PCI + PMI) for predicting the immediate recovery of urinary continence after RS-RARP was 0.726. Delong test showed that the area under the curve of the combined test for predicting the immediate urinary continence after RS-RARP was better compared with single parameter (p

Benefiting from the abrupt phase changes within subwavelength thicknesses, metasurfaces have been widely applied for lightweight and compact optical systems. Simultaneous broadband and high-efficiency characteristics are highly attractive for the practical implementation of metasurfaces. However, current metasurface devices mostly adopt discrete micro/nano structures, which rarely realize both merits simultaneously. In this paper, dielectric metasurfaces composed of quasi-continuous nanostrips are proposed to overcome this limitation. Via quasi-continuous nanostrips metasurface, a normal focusing metalens and a superoscillatory lens overcoming the diffraction limit are designed and experimentally demonstrated. The quasi-continuous metadevices can operate in a broadband wavelength ranging from 450 nm to 1000 nm and keep a high power efficiency. The average efficiency of the fabricated metalens reaches 54.24%, showing a significant improvement compared to the previously reported metalenses with the same thickness. The proposed methodology can be easily extended to design other metadevices with the advantages of broadband and high-efficiency in practical optical systems.

Abstract With the development of Non-Intrusive Load Monitoring (NILM), it has become feasible to perform device identification, energy consumption decomposition, and load switching detection using Deep Learning (DL) methods. Similar to other machine learning problems, the research and validation of NILM necessitate substantial data support. Moreover, different regions exhibit distinct characteristics in their electricity environments. Therefore, there is a need to provide open datasets tailored to different regions. In this paper, we introduce the Transient Dataset of Household Appliances with Intensive Switching Events (TDHA25). This dataset comprises switch instantaneous data from 10 typical household appliances in China. The TDHA dataset features a high sampling rate, accurate labelling, and realistic representation of actual appliance start-up waveforms. Additionally, appliance switching is achieved through precise control of relay switches, thus mitigating interference caused by mechanical switches. By furnishing such a dataset, we aim not only to enhance the recognition accuracy of existing NILM algorithms but also to facilitate the application of NILM algorithms in regions sharing similar electricity consumption characteristics to those of China.

The timely and accurate acquisition of crop-growth information is a prerequisite for implementing intelligent crop-growth management, and portable multispectral imaging devices offer reliable tools for monitoring field-scale crop growth. To meet the demand for obtaining crop spectra information over a wide band range and to achieve the real-time interpretation of multiple growth characteristics, we developed a novel portable snapshot multispectral imaging crop-growth sensor (PSMICGS) based on the spectral sensing of crop growth. A wide-band co-optical path imaging system utilizing mosaic filter spectroscopy combined with dichroic mirror beam separation is designed to acquire crop spectra information over a wide band range and enhance the device’s portability and integration. Additionally, a sensor information and crop growth monitoring model, coupled with a processor system based on an embedded control module, is developed to enable the real-time interpretation of the aboveground biomass (AGB) and leaf area index (LAI) of rice and wheat. Field experiments showed that the prediction models for rice AGB and LAI, constructed using the PSMICGS, had determination coefficients (R²) of 0.7 and root mean square error (RMSE) values of 1.611 t/ha and 1.051, respectively. For wheat, the AGB and LAI prediction models had R² values of 0.72 and 0.76, respectively, and RMSE values of 1.711 t/ha and 0.773, respectively. In summary, this research provides a foundational tool for monitoring field-scale crop growth, which is important for promoting high-quality and high-yield crops.

High-throughput phenotypic analysis plays an increasingly important role in crop breeding. In such research, the breeder usually establishes hundreds to thousands of plots, with each plot having its independent genetic breeding sources. The breeding plot extraction of genetic sources is usually outlined manually on RGB UAV imagery, which is time-consuming and subject to human bias. Therefore, a rapid method to extract the breeding plot for each genetic source in high-throughput phenotypic analysis would be very significant. In this paper, we propose a transferable method for extracting breeding plots from UAV RGB imagery. We utilized the fully convolutional neural network model A-UNet with an attention gate. After obtaining binary raster data from deep learning, we introduced post-processing. Subsequently, the raster data after post-processing were converted to vector data to obtain geographical coordinates. Finally, the UAV imagery was masked by the vector data to obtain the extraction results for each plot. The results showed that A-UNet achieved accuracies of over 90 % in precision, recall, and F1 score. Post-processing resulted in a 93 % average IoU in breeding plot extraction in the main study area. The average IOU achieved over 86 % in different spatial resolutions (1.6 cm and 0.4 cm), plot sizes (1 m × 1.5 and 2 m × 5 m), and crop types (rice). In summary, this study developed a method for extracting breeding plots in high-throughput phenotype analysis, which would help to be used as a high-throughput screening technique for accelerating crop breeding.

This paper mainly reviews the history and prospects of the atmospheric motion vector (AMV) of meteorological satellites. The development history of AMV and some milestone events are first introduced before briefly discussing them in the contexts of China, the United States, Europe, and Japan. The first section provides a detailed summary of the characteristics and key technologies of various traditional AMV algorithms, introduces the cross-correlation, pattern recognition, and nested tracking approaches, and describes five commonly used height assignment algorithms and their basic principles. The second section discusses several recently developed AMV products based on computer vision and machine learning technologies and introduces the advantages and research histories of the optical flow method, and three-dimensional and mesoscale AMVs. Finally, we compare the advantages and disadvantages of new and traditional AMV algorithms before examining the potential for future applications and development trends. We specifically highlight the higher spatial resolution obtained by the advanced optical flow method, better wind field information from three-dimensional AMV, and finer spatial and temporal resolutions of special weather from mesoscale AMV such as cyclones. Furthermore, we predict more promising three-dimensional and mesoscale AMVs in the upcoming future.

The Fengyun 4B (FY-4B) as an operational satellite was launched on 3 June 2021. The advanced geostationary radiation imager (AGRI) aboard on the FY-4B has ability to monitor aerosols information. The objective of this study is to introduce the operational algorithm for retrieving land aerosol based on FY-4B/AGRI data, which is called as an adaptive dark-target algorithm. First, on the basis of FY-4B/AGRI cloud mask and snow cover products, a novelty identification scheme was proposed to classify the pixels into cloud, haze and clear categories. Then, aiming at the characteristics of FY-4B/AGRI instrument, the adaptive relationships of surface reflectance varying with the normalized vegetation index or view zenith angle were established by atmospheric correction. By validating the AGRI retrievals every 15 min with AERONET AOD, AGRI performance (R: 0.95, bias: –0.014, RMSE: 0.091, within expected error (EE:): 81.99%) was comparable to that of Aqua/MODIS deep blue AOD (R: 0.95, bias: 0.024, RMSE: 0.095, EE: 78.57%), while have the largest difference with those of MYDDT. At daily mean scale, the statistics of AGRI AOD against AERONET performed well (R: 0.93, bias: 0.062, RMSE: 0.18, EE: 64%). Additionally, the agreement of AGRI retrievals with AERONET AOD varied with observation moments and stations. The FY-4B/AGRI operational aerosol product based on adaptive dark target algorithm were proved to be robust.

In this paper, we propose a two-stage transmission hardening and planning (TH&P) model that can meet the load growth demand of normal scenarios and the resilience requirements of hurricane-induced damage scenarios. To better measure the resilience requirements, the proposed TH&P model includes two resilience assessment indexes, namely, the load shedding (LS) under the damage scenario and the average connectivity degree (ACD) at different stages. The first-stage model, which aims to meet the load growth demand while minimizing the LS, is formulated as a mixed-integer linear program (MILP) to minimize the total planning and hardening cost of transmission lines, the operating cost of generators, and the penalty cost of wind power and load shedding in both normal and damage scenarios. The second-stage model aims to further improve the ACD when the ACD of the scheme obtained from the first-stage model cannot reach the target. Specifically, the contribution of each transmission line to the ACD is calculated, and the next hardened line is determined to increase the ACD. This process is repeated until the ACD meets the requirements. Case studies of the modified IEEE RTS-24 and two-area IEEE reliability test system-1996 indicate the proposed TH&P model can meet the requirements for both normal and damage scenarios.

With the increasing amount of space debris in near-earth space, the surveillance of space has become more crucial to prevent potential space collisions. In an optical surveillance system, a multitude of debris with varying sizes, speeds, and intensities exists within images, which brings great difficulties for the extraction of targets. Current methodologies mainly emphasize local saliency enhancement and source extraction based on global statistical attributes, yet often overlook the distribution similarity of individuals and how to map them in a standardized space. To address this limitation, this article introduces an innovative transformation from the grayscale space to the correlation space. Initially, a fourfold-structure model is designed to measure the local correlation for each element in the grayscale space. Subsequently, the standardized correlation space is constructed by correlation measurement. Then, combining an adaptive threshold choosing method based on statistics and a threshold limitation strategy based on correlation, the individuals can be separated from the feature space. Finally, a deblending method is applied to disentangle merged individuals based on the local gradient correlation. Thoroughly assessed using ten subimage sequences featuring targets with typical traits against complex backgrounds, the results confirm its superior extraction capabilities and effectiveness when compared to ten common baseline methods, which achieves an average 90.9% true positive rate in just 2.8 ms. Furthermore, the extraction outcomes serve as a basis for detecting true target trajectories in continuous sequences. This integration holds significant implications for practical applications in space surveillance engineering.

Abstract The number of seedlings is an important indicator that reflects the size of the wheat population during the seedling stage. Researchers increasingly use deep learning to detect and count wheat seedlings from unmanned aerial vehicle (UAV) images. However, due to the small size and diverse postures of wheat seedlings, it can be challenging to estimate their numbers accurately during the seedling stage. In most related works in wheat seedling detection, they label the whole plant, often resulting in a higher proportion of soil background within the annotated bounding boxes. This imbalance between wheat seedlings and soil background in the annotated bounding boxes decreases the detection performance. This study proposes a wheat seedling detection method based on a local annotation instead of a global annotation. Moreover, the detection model is also improved by replacing convolutional and pooling layers with the Space-to-depth Conv module and adding a micro-scale detection layer in the YOLOv5 head network to better extract small-scale features in these small annotation boxes. The optimization of the detection model can reduce the number of error detections caused by leaf occlusion between wheat seedlings and the small size of wheat seedlings. The results show that the proposed method achieves a detection accuracy of 90.1%, outperforming other state-of-the-art detection methods. The proposed method provides a reference for future wheat seedling detection and yield prediction.

In recent years, it is not uncommon for swelling soil landslides to occur again after treatment, which has seriously affected the safe operation of highways and railways. The degree of consolidation and cementation of swelling soil is poor, and its fractures are developed and it has a certain expansibility. Under the action of expansion force, its shear strength is obviously reduced. Thus, anti-slide pile support is used to control swelling soil landslides in this study. Based on the geological conditions and genetic mechanism of swelling soil landslide, a three-dimensional geological model with a width of 40 m is established to simulate the interaction mechanism and influencing factors between soil and pile in the process of anti-slip pile support. The results show that the larger the cohesion and internal friction angle, the stronger the soil arch effect, but the sensitivity is higher when the value is small. Therefore, attention should be paid to the weakening effect of soil arch effect in soil with low shear strength. The larger the pile spacing, the less obvious the soil arch effect. The swelling force also has a great influence on the soil arch effect from scratch. With the increase of the expansive force, the soil arch effect is weakened and completely disappears. Therefore, the adverse effects of expansive force should be considered, when designing anti-slide piles in swelling soil areas.

Plant stomata regulate transpiration (T) and CO2 assimilation, essential for the water–carbon cycle. Quantifying how environmental factors influence stomatal conductance will provide a scientific basis for understanding the vegetation–atmosphere water–carbon exchange process and water use strategies. Based on eddy covariance and hydro-metrological observations from FLUXNET sites with four plant functional types and using three widely applied methods to estimate ecosystem T from eddy covariance data, namely uWUE, Perez-Priego, and TEA, we quantified the regulation effect of environmental factors on canopy stomatal conductance (Gs). The environmental factors considered here include radiation (net radiation and solar radiation), water (soil moisture, relative air humidity, and vapor pressure deficit), temperature (air temperature), and atmospheric conditions (CO2 concentration and wind speed). Our findings reveal variation in the influence of these factors on Gs across biomes, with air temperature, relative humidity, soil water content, and net radiation being consistently significant. Wind speed had the least influence. Incorporating the leaf area index into a Random Forest model to account for vegetation phenology significantly improved model accuracy (R2 increased from 0.663 to 0.799). These insights enhance our understanding of the primary factors influencing stomatal conductance, contributing to a broader knowledge of vegetation physiology and ecosystem functioning.

Timely and accurate predictions of winter wheat yields are key to ensuring food security. In this research, winter wheat yield prediction models for six provinces were established using a random forest (RF) model. Two methods were employed to analyze feature variables. RF partial dependence plots were generated to demonstrate the nonlinear relationships between the feature variables and yield, and bivariate Moran’s I was considered to identify the spatial associations between variables. Results showed that when environmental data from key growth periods were used for prediction model establishment, the root mean square error (RMSE) varied between 200 and 700 kg/ha, and the coefficient of determination (R2) exceeded 0.5. Feature variable analysis results indicated that the longitude, latitude, topography and normalized difference vegetation index (NDVI) were important variables. Below the threshold, the yield gradually increased with increasing NDVI. Bivariate Moran’s I results showed that there was zonal distribution of meteorological elements. Within a large spatial range, the change in environmental variables due to the latitude and longitude should be accounted for in modeling, but the influence of collinearity between the feature variables should be eliminated via variable importance analysis.

This study focuses on evaluating the influence of the energy accumulation hole diameter on bi-directional cumulative tension blasting. Firstly, the penetration depth of bi-directional cumulative tension blasting is determined, followed by an analysis of the corresponding fracture mechanics behavior. Secondly, the Smooth Particle Hydrodynamics (SPH) method is used for numerical analysis of the bi-directional cumulative tension blasting process, and the Johnson-Holmquist constitutive model is then employed to examine the dynamic process during tensile blasting-induced cracking. This analysis provides insights into damage development, particle distribution, stress distribution, and crack propagation in the rock at different opening diameters. The findings reveal that, except for the 2 mm case, bi-directional cumulative tension blasting effectively produces directional cracks aligned with the energy accumulation direction. For hole diameters between 4–8 mm, linear through cracks form in the energy accumulation direction. However, a 2 mm diameter opening only generates short shear cracks around the blast hole. With energy accumulation hole diameters ranging from 10–14 mm, the crack propagation depth is insufficient for complete penetration, despite the presence of linear cracks in the energy accumulation direction. When diameter exceeds 14 mm, symmetrical airfoil cracks appear in non-concentrated energy directions, with larger diameters resulting in shorter crack propagation lengths. During the directional cracking process for hole diameters of 4–8 mm, explosive particles facilitate crack expansion in width and length through the action of a “gas wedge.” On-site blasting tests confirm the excellent directional pre-splitting effect of bi-directional cumulative tension blasting.

The rapid and accurate estimation of leaf area index (LAI) through remote sensing holds significant importance for precise crop management. However, the direct construction of a vegetation index model based on multi-spectral data lacks robustness and spatiotemporal expansibility, making its direct application in practical production challenging. This study aimed to establish a simple and effective method for LAI estimation to address the issue of poor accuracy and stability that is encountered by vegetation index models under varying conditions. Based on seven years of field plot trials with different varieties and nitrogen fertilizer treatments, the Kalman filter (KF) fusion method was employed to integrate the estimated outcomes of multiple vegetation index models, and the fusion process was investigated by comparing and analyzing the relationship between fixed and dynamic variances alongside the fusion accuracy of optimal combinations during different growth stages. A novel multi-model integration fusion method, KF-DGDV (Kalman Filtering with Different Growth Periods and Different Vegetation Index Models), which combines the growth characteristics and uncertainty of LAI, was designed for the precise monitoring of LAI across various growth phases of rice. The results indicated that the KF-DGDV technique exhibits a superior accuracy in estimating LAI compared with statistical data fusion and the conventional vegetation index model method. Specifically, during the tillering to booting stage, a high R2 value of 0.76 was achieved, while at the heading to maturity stage, it reached 0.66. In contrast, within the framework of the traditional vegetation index model, the red-edge difference vegetation index (DVIREP) model demonstrated a superior performance, with an R2 value of 0.65, during tillering to booting stage, and 0.50 during the heading to maturity stage, respectively. The multi-model integration method (MME) yielded an R2 value of 0.67 for LAI estimation during the tillering to booting stage, and 0.53 during the heading to maturity stage. Consequently, KF-DGDV presented an effective and stable real-time quantitative estimation method for LAI in rice.

The detection of cracks is extremely important for maintenance of concrete structures. Deep learning-based segmentation models have achieved high accuracy in crack segmentation. However, mainstream crack segmentation models have very high computational complexity, and therefore cannot be used in portable crack detection equipment. To address this problem, a knowledge distilling structure is designed by us. In this structure, a large teacher model named TBUNet is proposed to transfer crack knowledge to a student model with symmetry structure named ULNet. In the TBUNet, stacked transformer modules are used to capture dependency relationships between different crack positions in feature maps and achieve contextual awareness. In the ULNet, only a tiny U-Net with light-weighted parameters is used to maintain very low computational complexity. In addition, a mixed loss function is designed to ensure detail and global features extracted by the teacher model are consistent with those of the student model. Our designed experiments demonstrate that the ULNet can achieve accuracies of 96.2%, 87.6%, and 75.3%, and recall of 97.1%, 88.5%, and 76.2% on the Cracktree200, CRACK500, and MICrack datasets, respectively, which is 4–6% higher than most crack segmentation models. However, the ULNet only has a model size of 1 M, which is suitable for use in portable crack detection equipment.

Abstract Colorectal cancers (CRCs) are prevalent worldwide, yet current treatments remain inadequate. Using chemical genetic screens, we identify that co-inhibition of topoisomerase I (TOP1) and NEDD8 is synergistically cytotoxic in human CRC cells. Combination of the TOP1 inhibitor irinotecan or its bioactive metabolite SN38 with the NEDD8-activating enzyme inhibitor pevonedistat exhibits synergy in CRC patient-derived organoids and xenografts. Mechanistically, we show that pevonedistat blocks the ubiquitin/proteasome-dependent repair of TOP1 DNA-protein crosslinks (TOP1-DPCs) induced by TOP1 inhibitors and that the CUL4-RBX1 complex (CRL4) is a prominent ubiquitin ligase acting on TOP1-DPCs for proteasomal degradation upon auto-NEDD8 modification during replication. We identify DCAF13, a DDB1 and Cullin Associated Factor, as the receptor of TOP1-DPCs for CRL4. Our study not only uncovers a replication-coupled ubiquitin-proteasome pathway for the repair of TOP1-DPCs but also provides molecular and translational rationale for combining TOP1 inhibitors and pevonedistat for CRC and other types of cancers.

Abstract Combination of anti-cancer drugs is broadly seen as way to overcome the often-limited efficacy of single agents. The design and testing of combinations are however very challenging. Here we present a uniquely large dataset screening over 5000 targeted agent combinations across 81 non-small cell lung cancer cell lines. Our analysis reveals a profound heterogeneity of response across the tumor models. Notably, combinations very rarely result in a strong gain in efficacy over the range of response observable with single agents. Importantly, gain of activity over single agents is more often seen when co-targeting functionally proximal genes, offering a strategy for designing more efficient combinations. Because combinatorial effect is strongly context specific, tumor specificity should be achievable. The resource provided, together with an additional validation screen sheds light on major challenges and opportunities in building efficacious combinations against cancer and provides an opportunity for training computational models for synergy prediction.

Abstract Background Detecting and counting wheat spikes is essential for predicting and measuring wheat yield. However, current wheat spike detection researches often directly apply the new network structure. There are few studies that can combine the prior knowledge of wheat spike size characteristics to design a suitable wheat spike detection model. It remains unclear whether the complex detection layers of the network play their intended role. Results This study proposes an interpretive analysis method for quantitatively evaluating the role of three-scale detection layers in a deep learning-based wheat spike detection model. The attention scores in each detection layer of the YOLOv5 network are calculated using the Gradient-weighted Class Activation Mapping (Grad-CAM) algorithm, which compares the prior labeled wheat spike bounding boxes with the attention areas of the network. By refining the multi-scale detection layers using the attention scores, a better wheat spike detection network is obtained. The experiments on the Global Wheat Head Detection (GWHD) dataset show that the large-scale detection layer performs poorly, while the medium-scale detection layer performs best among the three-scale detection layers. Consequently, the large-scale detection layer is removed, a micro-scale detection layer is added, and the feature extraction ability in the medium-scale detection layer is enhanced. The refined model increases the detection accuracy and reduces the network complexity by decreasing the network parameters. Conclusion The proposed interpretive analysis method to evaluate the contribution of different detection layers in the wheat spike detection network and provide a correct network improvement scheme. The findings of this study will offer a useful reference for future applications of deep network refinement in this field.

Flat optical elements have attracted enormous attentions and act as promising candidates for the next generation of optical components. As one of the most outstanding representatives, liquid crystal (LC) has been widely applied in flat panel display industries and inspires the wavefront modulation with the development of LC alignment techniques. However, most LC elements perform only one type of optical manipulation and are difficult to realize the multifunctionality and light integration. Here, flat multifunctional liquid crystal elements (FMLCEs), merely composed of anisotropic LC molecules with space-variant orientations, are presented for multichannel information manipulation by means of polarization, space and wavelength multiplexing. Specifically, benefiting from the unique light response with the change of the incident polarization, observation plane, and working wavelength, a series of FMLCEs are demonstrated to achieve distinct near- and far-field display functions. The proposed strategy takes full advantage of basic optical parameters as the decrypted keys to improve the information capacity and security, and we expect it to find potential applications in information encryption, optical anti-counterfeiting, virtual/augmented reality, etc.

Abstract Background MiT-Renal Cell Carcinoma (RCC) is characterized by genomic translocations involving microphthalmia-associated transcription factor (MiT) family members TFE3, TFEB, or MITF. MiT-RCC represents a specific subtype of sporadic RCC that is predominantly seen in young patients and can present with heterogeneous histological features making diagnosis challenging. Moreover, the disease biology of this aggressive cancer is poorly understood and there is no accepted standard of care therapy for patients with advanced disease. Tumor-derived cell lines have been established from human TFE3-RCC providing useful models for preclinical studies. Methods TFE3-RCC tumor derived cell lines and their tissues of origin were characterized by IHC and gene expression analyses. An unbiased high-throughput drug screen was performed to identify novel therapeutic agents for treatment of MiT-RCC. Potential therapeutic candidates were validated in in vitro and in vivo preclinical studies. Mechanistic assays were conducted to confirm the on-target effects of drugs. Results The results of a high-throughput small molecule drug screen utilizing three TFE3-RCC tumor-derived cell lines identified five classes of agents with potential pharmacological efficacy, including inhibitors of phosphoinositide-3-kinase (PI3K) and mechanistic target of rapamycin (mTOR), and several additional agents, including the transcription inhibitor Mithramycin A. Upregulation of the cell surface marker GPNMB, a specific MiT transcriptional target, was confirmed in TFE3-RCC and evaluated as a therapeutic target using the GPNMB-targeted antibody-drug conjugate CDX-011. In vitro and in vivo preclinical studies demonstrated efficacy of the PI3K/mTOR inhibitor NVP-BGT226, Mithramycin A, and CDX-011 as potential therapeutic options for treating advanced MiT-RCC as single agents or in combination. Conclusions The results of the high-throughput drug screen and validation studies in TFE3-RCC tumor-derived cell lines have provided in vitro and in vivo preclinical data supporting the efficacy of the PI3K/mTOR inhibitor NVP-BGT226, the transcription inhibitor Mithramycin A, and GPNMB-targeted antibody-drug conjugate CDX-011 as potential therapeutic options for treating advanced MiT-RCC. The findings presented here should provide the basis for designing future clinical trials for patients with MiT-driven RCC.

The concept of adenoma-to-cancer transformation in human colorectal cancer (CRC) is widely accepted. However, the relationship between transcriptome features and adenoma to carcinoma transformation in canines is not clear. We collected transcriptome data from 8 normal colon tissues, 4 adenoma tissues, and 15 cancer tissues. Differential analysis was unable to determine the dynamic changes of genes but revealed that PFKFB3 may play a key role in this process. Enrichment analysis explained metabolic dysregulation, immunosuppression, and typical cancer pathways in canine colorectal tumors. MFuzz generated specific dynamic expression patterns of five differentially expressed genes (DEGs). Weighted correlation network analysis showed that DEGs in cluster 3 were associated with malignant tissues, revealing the key role of inflammatory and immune pathways in canine CRC, and the S100A protein family was also found to be involved in the malignant transformation of canine colorectal tumors. By comparing strategies between humans and dogs, we found five novel markers that may be drivers of CRC. Among them, GTBP4 showed excellent diagnostic and prognostic ability. This study was the first systematic exploration of transformation in canine CRC, complemented the molecular characteristics of the development and progression of canine CRC, and provided new potential biomarkers and comparative oncologic evidence for biomarker studies in human colorectal cancer.

Precise land use land cover (LULC) data are essential for understanding the landscape structure and spatial pattern of land use/cover in the Yellow River Basin (YRB) to regulate scientific and rational territorial spatial planning and support sustainable development. However, differences in the multiple sets of LULC products in portraying the land composition of the YRB limit our understanding of the land cover composition in this region. To address this issue, this study chose five sets of open and high spatiotemporal LULC data in 2020, namely, CLCD, LSV10, ESRI10, CLC_FCS30, and Globeland30, to evaluate the accuracy and consistency of classification in the YRB. Our results show that: (1) The LULC composition of the YRB in 2020 was mapped consistently by the five datasets. Grasslands, croplands, and woodlands constitute the major LULC types, accounting for 96% of the total area of the study area. (2) The correlation coefficients of the LULC types of any two of the five datasets ranged from 0.926 to 0.998, showing high land compositional consistency. However, among the five datasets, there were considerable differences in the areas of a single LULC type. (3) The classification consistencies of croplands, woodlands, grasslands, and water bodies were higher than 60% in any two datasets. The spatial consistencies of grasslands, croplands, and woodlands were higher than those of other LULC types. An area with better consistency can reach more than 50% of the average area of the corresponding land types, but grasslands were mixed with other LULC types in ESRI10 and GLC_FCS30. (4) According to the accuracy assessments, LSV10 data have the highest overall classification accuracy, 79.32%, and the classification accuracy of major land types is also higher than 70%; GLC_FCS30 data have the lowest overall accuracy, 70.14%. Based on these results, LSV10 can more accurately demonstrate LULC than the other four datasets. This study can be used as a reference for selecting land cover data, and it also highlights that the necessary assessments of consistency and accuracy are essential when conducting land use/cover change studies in a specific region.

Abstract Background Reptiles exhibit a wide variety of skin colors, which serve essential roles in survival and reproduction. However, the molecular basis of these conspicuous colors remains unresolved. Results We investigate color morph-enriched Asian vine snakes (Ahaetulla prasina), to explore the mechanism underpinning color variations. Transmission electron microscopy imaging and metabolomics analysis indicates that chromatophore morphology (mainly iridophores) is the main basis for differences in skin color. Additionally, we assemble a 1.77-Gb high-quality chromosome-anchored genome of the snake. Genome-wide association study and RNA sequencing reveal a conservative amino acid substitution (p.P20S) in SMARCE1, which may be involved in the regulation of chromatophore development initiated from neural crest cells. SMARCE1 knockdown in zebrafish and immunofluorescence verify the interactions among SMARCE1, iridophores, and tfec, which may determine color variations in the Asian vine snake. Conclusions This study reveals the genetic associations of color variation in Asian vine snakes, providing insights and important resources for a deeper understanding of the molecular and genetic mechanisms related to reptilian coloration.

Hardening planning is an effective way to enhance the ability of the transmission system against hurricane attacks and further improve the resilience of power systems. However, the existing hardening method gives little consideration to the risk of multiple failures under hurricanes which may underestimate the real post-disaster damage. To fill the research gap, this paper proposes a resilience index, i.e. fault chain risk expectation (FCRE), which can measure the risk of multiple failures under hurricanes. Then the FCRE-based resilient transmission hardening planning (RTHP) model is proposed to minimize hardening and load shedding penalty costs under different hurricane scenarios. In this paper, hurricane simulation is finished by critical parameters firstly, then the failure probability curve is adopted to obtain the spatial–temporal failure probability of different components, and finally, the corresponding contingency scenarios under simulated hurricanes are obtained by Monte Carlo Simulation (MCS) and k-means clustering algorithms. Meanwhile, the set of fault chains is constructed to calculate the FCRE for each line, and the corresponding constraint is formed to ensure that the system resilience reaches a target level. The proposed model is verified on the modified RTS 24-bus system and the results demonstrate the effectiveness of the proposed hardening planning method.

Abstract Autism spectrum disorder (ASD) affects 1–2% of all children and poses a great social and economic challenge for the globe. As a highly heterogeneous neurodevelopmental disorder, the development of its treatment is extremely challenging. Multiple pathways have been linked to the pathogenesis of ASD, including signaling involved in synaptic function, oxytocinergic activities, immune homeostasis, chromatin modifications, and mitochondrial functions. Here, we identify secretagogin (SCGN), a regulator of synaptic transmission, as a new risk gene for ASD. Two heterozygous loss-of-function mutations in SCGN are presented in ASD probands. Deletion of Scgn in zebrafish or mice leads to autism-like behaviors and impairs brain development. Mechanistically, Scgn deficiency disrupts the oxytocin signaling and abnormally activates inflammation in both animal models. Both ASD probands carrying Scgn mutations also show reduced oxytocin levels. Importantly, we demonstrate that the administration of oxytocin and anti-inflammatory drugs can attenuate ASD-associated defects caused by SCGN deficiency. Altogether, we identify a convergence between a potential autism genetic risk factor SCGN, and the pathological deregulation in oxytocinergic signaling and immune responses, providing potential treatment for ASD patients suffering from SCGN deficiency. Our study also indicates that it is critical to identify and stratify ASD patient populations based on their disease mechanisms, which could greatly enhance therapeutic success.

Spacecraft pose estimation using computer vision has garnered increasing attention in research areas such as automation system theory, control theory, sensors and instruments, robot technology, and automation software. Confronted with the extreme environment of space, existing spacecraft pose estimation methods are predominantly multi-stage networks with complex operations. In this study, we propose an approach for spacecraft homography pose estimation with a single-stage deep convolutional neural network for the first time. We formulated a homomorphic geometric constraint equation for spacecraft with planar features. Additionally, we employed a single-stage 2D keypoint regression network to obtain homography 2D keypoint coordinates for spacecraft. After decomposition to obtain the rough spacecraft pose based on the homography matrix constructed according to the geometric constraint equation, a loss function based on pixel errors was employed to refine the spacecraft pose. We conducted extensive experiments using widely used spacecraft pose estimation datasets and compared our method with state-of-the-art techniques in the field to demonstrate its effectiveness.

Patents as proxy for technological trends is well noted. The rapid increase of patents in China, however, has aroused debates on its technological progress: ‘few original innovations’ are produced in advanced areas, and true ‘breakthroughs’ are disproportionate to the quantity of the applications. As different technological fields contributions vary to technological progress, a nuanced understanding towards technological composition is in need to help reveal China’s strength in technological innovation. This research takes the Yangtze River Delta (YRD), one of China’s most inventive city-regions, as an epitome to examine the issue via three steps: (1) valid patent inventions applied from 2010 to 2018 are sorted to capture the concentration and colocation features of 35 technological fields defined by the World Intellectual Property Organization (WIPO); (2) four types (intensive, extensive, distinctive, and supportive) of technologies exemplifying technology intensity and interactivity are identified by the cross-classification method and further analyzed by spatial autocorrelation; (3) how urban factors relate to innovation of these four types of technologies are explored. This research unveils a mixed but polarized structure of technological composition in the YRD where the spatial concentration of technologies is as analogous to the nation’s but colocation is not; though quite a few technologies fall into the intensive (usually high-tech) category which assumes to be more likely to breed ‘breakthroughs’, their numbers are limited and far less than extensive (usually labor intensive) or supportive. Knowledge exchange is frequent in core inventive cities where economic performance measured by GDP is most eminently linked to patent inventions of categorized technologies, the exception is intensive technology for which the significance of university students overrides other factors.

Utilizing neodymium-based butadiene rubber as a baseline, this study examines the effect of eco-friendly aromatic TDAE oil, fillers, and crosslinking reactions on neodymium-based rare-earth butadiene rubber (Nd-BR) crystallization behavior. The findings suggest that TDAE oil hinders crystallization, resulting in decreased crystallization temperatures and heightened activation energies (Ea). The crystallization activation energies for 20 parts per hundreds of rubber (PHR) and 37.5 PHR oil stand at −116.8 kJ/mol and −48.1 kJ/mol, respectively, surpassing the −264.3 kJ/mol of the unadulterated rubber. Fillers act as nucleating agents, hastening crystallization, which in turn elevates crystallization temperatures and diminishes Ea. In samples containing 20 PHR and 37.5 PHR oil, the incorporation of carbon black and silica brought the Ea down to −224.9 kJ/mol and −239.1 kJ/mol, respectively. Crosslinking considerably restricts molecular motion and crystallization potential. In the examined conditions, butadiene rubber containing 37.5 PHR oil displayed no crystallization following crosslinking, albeit crystallization was discernible with filler inclusion. Simultaneously, the crystallinity level sharply declined, manifesting cold crystallization behavior. The crosslinking process elevates Ea, while the equilibrium melting point (Tm0) noticeably diminishes. For instance, the Tm0 of pure Nd-BR is approximately −0.135 °C. When blended with carbon black and silica, the Tm0 values are −3.13 °C and −5.23 °C, respectively. After vulcanization, these values decrease to −21.6 °C and −10.16 °C. Evaluating the isothermal crystallization kinetics of diverse materials via the Avrami equation revealed that both the oil and crosslinking process can bring about a decrease in n values, with the Avrami index n for various samples oscillating between 1.5 and 2.5. Assessing the dynamic mechanical attributes of different specimens reveals that Nd-BR crystallization notably curtails its glass transition, marked by a modulus shift in the transition domain and a decrement in loss factor. The modulus in the rubbery state also witnesses a substantial augmentation.

This study investigates the influence of poly(butadiene-isoprene) copolymer rubber (BIR) and TDAE oil on the crystallization and melting behavior of neodymium-based butadiene rubber (Nd-BR). The study demonstrates that the melting points of Nd-BR and its blends decrease with lower crystallization temperatures. Below the critical crystallization temperature (Tc,c), the melting behavior shows dual peaks in distinct temperature ranges, which are attributed to different spherulitic sizes. The addition of BIR or TDAE oil lowers the Tc,c, with TDAE oil exerting a more substantial effect. These diluents mainly influence the nucleation temperature and crystallinity level of Nd-BR while having a minimal effect on the crystallization mechanism. A master curve, which overlaps for various samples, is developed by correlating the peak melting temperature (Tm,peak) with the Tc. This curve facilitates a quantitative assessment of the effects of BIR and TDAE oil on Nd-BR, highlighting the greater influence of TDAE oil on the crystalline structure compared with BIR at equivalent mass fractions. By applying the Lorentz equation and multi-peak fitting, a relationship between the melting points and crystallization temperatures is established, enabling the calculation of the equilibrium melting points (Tm0) for different samples. The findings show a reduction in the Tm0 due to the diluents; specifically, the Tm0 is approximately 0 °C for pure Nd-BR, and it decreases to −4.579 °C and −6.579 °C for samples with 50 PHR TDAE oil and 60 wt.% BIR, respectively.

Constructing a microstructure of red clay is an important part of studying the physical and mechanical properties of red clay. The study of red clay microstructure is generalized. The red clay matrix and pores are regarded as two types of randomly moving particles, and the microstructure model of three-dimensional red clay random porous media is established from the Langevin equation of the phase separation process in stochastic dynamics, using the separation and aggregation of the two particles. The model controls the evolution process of the porous medium by artificially controlling the particle placement. Here, the trends of porosity, average pore length, Euler’s number, and the fractal dimension of the porous medium during the evolution process under different parameter conditions (smooth length Δ and rise and fall term η) are calculated, and a feasible method for surviving the microstructure of red clay is summarized. Due to the consideration of the interaction forces between the solid- and void-phase particles, the porous media generated by this model are more similar to the real porous media in nature, with connected and unconnected pore structures and tortuous pore channels. Finally, the red clay of Bijie, Guizhou, is modeled as an example to prove the feasibility of the method.

Given that the effectiveness of COVID-19 vaccines and other therapies is greatly limited by the continuously emerging variants, non-pharmaceutical interventions have been adopted as primary control strategies in the global fight against the COVID-19 pandemic. However, implementing strict interventions over extended periods of time is inevitably hurting the economy. Many countries are faced with the dilemma of how to take appropriate policy actions for socio-economic recovery while curbing the further spread of COVID-19. With an aim to solve this multi-objective decision-making problem, we investigate the underlying temporal dynamics and associations between policies, mobility patterns, and virus transmission through vector autoregressive models and the Toda-Yamamoto Granger causality test. Our findings reveal the presence of temporal lagged effects and Granger causality relationships among various transmission and human mobility variables. We further assess the effectiveness of existing COVID-19 control measures and explore potential optimal strategies that strike a balance between public health and socio-economic recovery for individual states in the U.S. by employing the Pareto optimality and genetic algorithms. The results highlight the joint power of the state of emergency declaration, wearing face masks, and the closure of bars, and emphasize the necessity of pursuing tailor-made strategies for different states and phases of epidemiological transmission. Our framework enables policymakers to create more refined designs of COVID-19 strategies and can be extended to other countries regarding best practices in pandemic response.

Abstract Despite the high prevalence of Down syndrome (DS) and early identification of the cause (trisomy 21), its molecular pathogenesis has been poorly understood and specific treatments have consequently been practically unavailable. A number of medical conditions throughout the body associated with DS have prompted us to investigate its molecular etiology from the viewpoint of the embryonic organizer, which can steer the development of surrounding cells into specific organs and tissues. We established a DS zebrafish model by overexpressing the human DYRK1A gene, a highly haploinsufficient gene located at the “critical region” within 21q22. We found that both embryonic organizer and body axis were significantly impaired during early embryogenesis, producing abnormalities of the nervous, heart, visceral, and blood systems, similar to those observed with DS. Quantitative phosphoproteome analysis and related assays demonstrated that the DYRK1A-overexpressed zebrafish embryos had anomalous phosphorylation of β-catenin and Hsp90ab1, resulting in Wnt signaling enhancement and TGF-β inhibition. We found an uncovered ectopic molecular mechanism present in amniocytes from fetuses diagnosed with DS and isolated hematopoietic stem cells (HSCs) of DS patients. Importantly, the abnormal proliferation of DS HSCs could be recovered by switching the balance between Wnt and TGF-β signaling in vitro. Our findings provide a novel molecular pathogenic mechanism in which ectopic Wnt and TGF-β lead to DS physical dysplasia, suggesting potential targeted therapies for DS.

The energy transition is increasingly being discussed and implemented to cope with the growing environmental crisis. However, great challenges remain for effectively harvesting and utilizing solar energy in cities related to time and location-dependant supply and demand, which needs more accurate forecasting- and an in-depth understanding of the electricity production and dynamic balancing of the flexible energy supplies concerning the electricity market. To tackle this problem, this article discusses the development of solar cities over the past few decades and proposes a refined and enriched concept of a sustainable solar city with six integrated modules, namely, land surface solar irradiation, three-dimensional (3D) urban surfaces, spatiotemporal solar distribution on 3D urban surfaces, solar photovoltaic (PV) planning, solar PV penetration into different urban systems, and the consequent socio-economic and environmental impacts. In this context, Geographical Information Science (GIScience) demonstrates its potent capability in building the conceptualized solar city with a dynamic balance between power supply and demand over time and space, which includes the production of multi-sourced spatiotemporal big data, the development of spatiotemporal data modelling, analysing and optimization, and geo-visualization. To facilitate the development of such a solar city, this article from the GIScience perspective discusses the achievements and challenges of (i) the development of spatiotemporal big data used for solar farming, (ii) the estimation of solar PV potential on 3D urban surfaces, (iii) the penetration of distributed PV systems in digital cities that contains the effects of urban morphology on solar accessibility, optimization of PV systems for dynamic balancing between supply and demand, and PV penetration represented by the solar charging of urban mobility, and (iv) the interaction between PV systems and urban thermal environment. We suggest that GIScience is the foundation, while further development of GIS models is required to achieve the proposed sustainable solar city.

The crystallization behavior of neodymium-based rare earth polybutadiene rubber (Nd-BR) is studied in the presence of small-molecule treated distillate aromatic extract (TDAE) and high-molecular-weight polybutadiene–isoprene copolymer rubber (BIR). Pronounced inhibitory effects on the crystallization of Nd-BR are exhibited by both materials, as evidenced by reductions in the crystallization temperature (Tc), melting point (Tm), and corresponding enthalpy change. It is found that, at equal concentrations, a greater influence on the crystallization rate is exerted by TDAE oils, whereas nucleation inhibition is more potently affected by BIR. Incomplete crystallization during cooling is exhibited by Nd-BR when the TDAE oil concentration reaches 40 parts per hundreds of rubber (PHR) (31 wt.%), or BIR achieves a 60 wt.% concentration; subsequently, a noticeable cold crystallization phenomenon is observed upon heating. Insights into the isothermal crystallization kinetics are offered by the data, which reveal that the Avrami index n value for Nd-BR predominantly ranges between 2.5 and 3.0. A decrease in the n value is induced by a small amount of TDAE oil, while a noticeable decline in the n value is observed only when the BIR concentration is 60 wt.%. A correlation between the crystallization activation energy, the concentration of TDAE oil and BIR, and the crystallization temperature is established; a negative activation energy is recorded, and a decrease in the crystallization rate is noted when both concentrations are low and the crystallization temperature exceeds −50 °C. In contrast, positive activation energy and an increase in the crystallization rate are observed when the BIR concentration reaches 60%, and the crystallization temperature resides between −50 °C and −70 °C.

The authors uncover synthetic lethality between TP53 and ENDOD1, which contains an atypical endonuclease domain. Co-depleted G1 cells accumulate single stranded DNA and die. Combined with xenograft data the work identifies ENDOD1 as a potential cancer-specific SL drug discovery target.

Small cell lung cancer (SCLC) is a recalcitrant malignancy with limited treatment options. Bromodomain and extraterminal domain inhibitors (BETis) have shown promising preclinical activity in SCLC, but the broad sensitivity spectrum limits their clinical prospects. Here, we performed unbiased high-throughput drug combination screens to identify therapeutics that could augment the antitumor activities of BETis in SCLC. We found that multiple drugs targeting the PI-3K–AKT–mTOR pathway synergize with BETis, among which mTOR inhibitors (mTORis) show the highest synergy. Using various molecular subtypes of the xenograft models derived from patients with SCLC, we confirmed that mTOR inhibition potentiates the antitumor activities of BETis in vivo without substantially increasing toxicity. Furthermore, BETis induce apoptosis in both in vitro and in vivo SCLC models, and this antitumor effect is further amplified by combining mTOR inhibition. Mechanistically, BETis induce apoptosis in SCLC by activating the intrinsic apoptotic pathway. However, BET inhibition leads to RSK3 upregulation, which promotes survival by activating the TSC2-mTOR-p70S6K1-BAD cascade. mTORis block this protective signaling and augment the apoptosis induced by BET inhibition. Our findings reveal a critical role of RSK3 induction in tumor survival upon BET inhibition and warrant further evaluation of the combination of mTORis and BETis in patients with SCLC.

Accurate wheat spike detection is crucial in wheat field phenotyping for precision farming. Advances in artificial intelligence have enabled deep learning models to improve the accuracy of detecting wheat spikes. However, wheat growth is a dynamic process characterized by important changes in the color feature of wheat spikes and the background. Existing models for wheat spike detection are typically designed for a specific growth stage. Their adaptability to other growth stages or field scenes is limited. Such models cannot detect wheat spikes accurately caused by the difference in color, size, and morphological features between growth stages. This paper proposes WheatNet to detect small and oriented wheat spikes from the filling to the maturity stage. WheatNet constructs a Transform Network to reduce the effect of differences in the color features of spikes at the filling and maturity stages on detection accuracy. Moreover, a Detection Network is designed to improve wheat spike detection capability. A Circle Smooth Label is proposed to classify wheat spike angles in drone imagery. A new micro-scale detection layer is added to the network to extract the features of small spikes. Localization loss is improved by Complete Intersection over Union to reduce the impact of the background. The results show that WheatNet can achieve greater accuracy than classical detection methods. The detection accuracy with average precision of spike detection at the filling stage is 90.1%, while it is 88.6% at the maturity stage. It suggests that WheatNet is a promising tool for detection of wheat spikes.

Lithium metal batteries (LMBs) have recently been revitalized as one of the most promising electrochemical energy storage systems, owing to the ultrahigh specific capacity (3860 mAh g−1) and ultralow potential (−3.04 ​V vs. standard hydrogen electrode) of lithium metal anodes. However, safety hazards originating from lithium dendrite growth and pulverization during cycling and thermal stimulation present significant challenges to the practical application of LMBs. To address this issue, we have developed an in situ polymer electrolyte with thermally induced interfacial ion-blocking ability. We demonstrate that the repolymerization and deposition of residual vinylene carbonate in the as-prepared electrolyte under thermal abuse predominantly results in thermally induced ion blocking at the solid electrolyte interface, thus achieving superior LMB safety. The developed polymer electrolyte also yields superior cyclability in LMBs. This design philosophy provides a good paradigm for improving the safety of LMBs.

Magnesium metal and its alloys are being developed as effective orthopedic implants; however, the mechanisms underlying the actions of magnesium on bones remain unclear. Cystic fibrosis, the most common genetic disease in Caucasians caused by the mutation of CFTR, has shown bone disorder as a key clinical manifestation, which currently lacks effective therapeutic options. Here we report that implantation of magnesium-containing implant stimulates bone formation and improves bone fracture healing in CFTR-mutant mice. Wnt/β-catenin signaling in the bone is enhanced by the magnesium implant, and inhibition of Wnt/β-catenin by iCRT14 blocks the magnesium implant to improve fracture healing in CFTR-mutant mice. We further demonstrate that magnesium ion enters osteocytes, increases intracellular cAMP level and activates ATF4, a key transcription factor known to regulate Wnt/β-catenin signaling. In vivo knockdown of ATF4 abolishes the magnesium implant-activated β-catenin in bones and reverses the improved-fracture healing in CFTR-mutant mice. In addition, oral supplementation of magnesium activates ATF4 and β-catenin as well as enhances bone volume and density in CFTR-mutant mice. Together, these results show that magnesium implantation or supplementation may serve as a potential anabolic therapy for cystic fibrosis-related bone disease. Activation of ATF4-dependent Wnt/β-catenin signaling in osteocytes is identified as a previously undefined mechanism underlying the beneficial effect of magnesium on bone formation.

This paper proposes a voltage stability constrained optimal power flow (VSC-OPF) for an unbalanced distribution system with distributed generators (DGs) based on semidefinite programming (SDP). The AC optimal power flow (ACOPF) for unbalanced distribution systems is formulated as a chordal relaxation-based SDP model. The minimal singular value (MSV) of the power flow Jacobian matrix is adopted to indicate the voltage stability margin. The Jacobian matrix can be explicitly expressed by ACOPF state variables. The nonlinear constraint on the Jacobian MSV is then replaced with its maximal convex subset using linear matrix inequality (LMI), which can be incorporated in the SDP-based ACOPF formulation. A penalty technique is leveraged to improve the exactness of the SDP relaxation. Case studies performed on several IEEE test systems validate the effectiveness of the proposed method.

Soil moisture (SM) information can be routinely obtained from high-quality microwave retrievals at a global scale—such as datasets generated by the Soil Moisture Active Passive (SMAP) mission. In this article, using mutual information (MI) theory, we investigate the efficacy of the downscaled SMAP/Sentinel-1 L2 3-km EASE-Grid SM product (SPL2) for the detection of agricultural drought over northwestern China. The SPL2 is generated by merging SMAP enhanced radiometer data with Sentinel-1 radar observations. To evaluate the efficiency of the SPL2 downscaled algorithm, the SMAP Enhanced L3 Radiometer 9-km EASE-Grid SM product (SPL3) is also utilized as a non-downscaled baseline. Over croplands, comparing normalized MI (NMI) values sampled between the NDVI time series and 3-km Sentinel-1 C-band backscatter coefficient (σ) from SPL2 with NMI values between NDVI and SPL3 radiometer brightness temperature (Tb; resampled to 3-km resolution), we find that the Sentinel-1 σ explains more (3-km) NDVI information than the SPL3 Tb, as the NMI between σvh (σvv) and NDVI is 15% (8%), larger than that between SPL3 Tb and NDVI (5%). However, compared to the SPL3 Tb baseline, the information from downscaled SPL2 Tb on NDVI is reduced by approximately 3%, and the SPL2 algorithm extracts only 7% (10%) of the total information available from both enhanced SPL3 Tb and Sentinel-1 σvh (σvv). Overall, the C-band σ signal provides valuable information for vegetation monitoring due to its frequency advantage. However, additional efforts should be focused on SPL2 merging algorithms to improve the value of the downscaled SPL2 product for agricultural applications.

Crack detection plays a vital role in concrete surface maintenance. Deep-learning-based methods have achieved state-of-the-art results. However, these methods have some drawbacks. Firstly, a single-sized convolutional kernel in crack image segmentation tasks may result in feature information loss for small cracks. Secondly, only using linear interpolation or up-sampling to restore high-resolution features does not restore global information. Thirdly, these models are limited to learning edge features, causing edge feature information loss. Finally, various stains interfere with crack feature extraction. To solve these problems, a pyramid hierarchical convolution module (PHCM) is proposed by us to extract the features of cracks with different sizes. Furthermore, a mixed global attention module (MGAM) was used to fuse global feature information. Furthermore, an edge feature extractor module (EFEM) was designed by us to learn the edge features of cracks. In addition, a supplementary attention module (SAM) was used to resolv interference in stains in crack images. Finally, a pyramid hierarchical-convolution-based U-Net (PHCNet) with MGAM, EFEM, and SAM is proposed. The experimental results show that our PHCNet achieves accuracies of 0.929, 0.823, 0.989, and 0.801 on the Cracktree200, CRACK500, CFD, and OAD_CRACK datasets, respectively, which is higher than that of the traditional convolutional models.

Crack detection is an important task for road maintenance. Currently, convolutional neural-network-based segmentation models with attention blocks have achieved promising results, for the reason that these models can avoid the interference of lights and shadows. However, by carefully examining the structure of these models, we found that these segmentation models usually use down-sampling operations to extract high-level features. This operation reduces the resolution of features and causes feature information loss. Thus, in our proposed method, a Parallel Convolution Module (PCM) was designed to avoid feature information loss caused by down-sampling. In addition, the attention blocks in these models only focused on selecting channel features or spatial features, without controlling feature information flow. To solve the problem, a Self-Gated Attention Block (SGAB) was used to control the feature information flow in the attention block. Therefore, based on the ideas above, a PSNet with a PCM and SGAB was proposed by us. Additionally, as there were few public datasets for detailed evaluation of our method, we collected a large dataset by ourselves, which we named the OAD_CRACK dataset. Compared with the state-of-the-art crack detection method, our proposed PSNet demonstrated competitive segmentation performance. The experimental results showed that our PSNet could achieve accuracies of 92.6%, 81.2%, 98.5%, and 76.2% against the Cracktree200, CRACK500, CFD, and OAD_CRACK datasets, respectively, which were 2.6%, 4.2%, 1.2%, and 3.3% higher than those of the traditional attention models.

Rhabdomyosarcoma (RMS) is the most common pediatric soft tissue sarcoma. Despite decades of clinical trials, the overall survival rate for patients with relapsed and metastatic disease remains below 30%, underscoring the need for novel treatments. FGFR4, a receptor tyrosine kinase that is overexpressed in RMS and mutationally activated in 10% of cases, is a promising target for treatment. Here, we show that futibatinib, an irreversible pan-FGFR inhibitor, inhibits the growth of RMS cell lines in vitro by inhibiting phosphorylation of FGFR4 and its downstream targets. Moreover, we provide evidence that the combination of futibatinib with currently used chemotherapies such as irinotecan and vincristine has a synergistic effect against RMS in vitro. However, in RMS xenograft models, futibatinib monotherapy and combination treatment have limited efficacy in delaying tumor growth and prolonging survival. Moreover, limited efficacy is only observed in a PAX3-FOXO1 fusion-negative (FN) RMS cell line with mutationally activated FGFR4, whereas little or no efficacy is observed in PAX3-FOXO1 fusion-positive (FP) RMS cell lines with FGFR4 overexpression. Alternative treatment modalities such as combining futibatinib with other kinase inhibitors or targeting FGFR4 with CAR T cells or antibody-drug conjugate may be more effective than the approaches tested in this study.

Recent urban transformations have led to critical reflections on the blighted urban infrastructures and called for re-stimulating vital urban places. Especially, the metro has been recognized as the backbone infrastructure for urban mobility and the associated economy agglomeration. To date, limited research has been devoted to investigating the relationship between metro vitality and built environment in mega-cities empirically. This paper presents a multisource urban data-driven approach to quantify the metro vibrancy and its association with the underlying built environment. Massive smart card data is processed to extract metro ridership, which denotes the vibrancy around the metro station in physical space. Social media check-ins are crawled to measure the vitality of metros in virtual spaces. Both physical and virtual vibrancy are integrated into a holistic metro vibrancy metric using an entropy-based weighting method. Certain built environment characteristics, including land use, transportation and buildings are modeled as independent variables. The significant influences of built environmental factors on the metro vibrancy are unraveled using the ordinary least square regression and the spatial lag model. With experiments conducted in Shenzhen, Singapore and London, this study comes up with a conclusion that spatial distributions of metro vibrancy metrics in three cities are spatially autocorrelated. The regression analysis suggests that in all the three cities, more affluent urban areas tend to have higher metro virbrancy, while the road density, land use and buildings tend to impact metro vibrancy in only one or two cities. These results demonstrate the relationship between the metro vibrancy and built environment is affected by complex urban contexts. These findings help us to understand metro vibrancy thus make proper policy to re-stimulate the important metro infrastructure in the future.

Piedmont zones have been witnessing intensive human activities since ancient times. However, it remains unclear when it comes to the environmental mechanism for early humans exploiting piedmont zones. Here we present a case study about the interactions between early human activities and landscape evolution in the piedmont of Taihang Mountain, an area with prominent ecological and cultural significance in Chinese history. Based on chronological and pollen analyses, we reconstruct the regional landscape evolution in the Fengtougang (FTG) site of southern Taihang Mountain during the Holocene. The results show that the area has been dominated by terrestrial plants since the late Longshan culture (4000 BP), including a large number of Pinus, Artemisia, Spiraea, and Gramineae, a few Cattails, and some other aquatic herbs. During the early history (4000-2000 BP), there is a combination of Pinus, Artemisia, Spiraea, Compositae, and Selaginella Chinensis, with a few aquatic plants. Since the late history (500 BP), the Chinese selaginella, Pinus, Selaginella, and Sedge families dominate, with no aquatic plant pollen found. Combining the detailed geoarchaeological survey, grain size analysis, and magnetic susceptibility analysis, we demonstrate that there should be a landscape of extensive floodplain during the early-middle Holocene (10000-4000 BP). During the late Longshan culture (about 4000 BP), the study area should be dominated by a landscape of sparse forest grassland with interlacing rivers and lakes. With river downcutting and watercourse fixation since the late Longshan culture, the flooded area massively shrinks, providing suitable habitat for human settlement. From then on, human activities begin to move to the study area on a large scale, followed by continuous cultural development and thriving early civilization.

Artificial irrigation is critical for improving soil moisture conditions and ensuring crop growth. Its irrational deployment can lead to ecological and environmental issues. Mapping and understanding the changes in irrigated areas are vital to effectively managing limited water. However, most researchers map irrigated areas with a single data resource, which makes it hard to detect irrigated signals in complex situations. The case study area for this paper was China’s winter wheat region, and an irrigated area map was generated by analyzing the effects of artificial irrigation on crop phenological characteristics and soil moisture time series. The mapping process involved three steps: (1) generating a basic irrigated map by employing the ISODATA classification method on the Kolmogorov–Smirnov test irrigation signals from the microwave remote sensing data and reanalysis data; (2) creating the other map with the maximum likelihood ratio classification and zoning scheme on the phenological parameters extracted from the NDVI time series; and (3) fusing these two maps at the decision level to obtain the final map with a higher spatial resolution of 1 km. The map was evaluated against existing irrigated area data and was highly compatible with GMIA 5.0. The overall accuracy (OA) was 73.49%.

Although space targets have different shapes, sizes and intensities, their distributions share certain commonalities. However, it is difficult to summarize a generalized distribution function for space targets. Moreover, most of the existing methods based on deep learning are not suitable to use directly because of the size of targets and the cost of manual labeling for a full image. In this paper, we proposed a pattern for space target detection based on a convolutional neural network (CNN) to learn essential features of the targets from data. In the processing stage, the background is estimated and removed. Then, image techniques are used to search and process region proposals. Different sizes of region proposals are recognized by a discriminator, which is built upon a small CNN trained with the data of several specific targets. Finally, a non-maximum suppression (NMS) operation is used to remove redundant targets. In the network structure, to further enhance the influence of the effective area, the parameters calculated from the center region of the input are utilized as guidance information and added to the features before the full connection. Moreover, the bias loss is applied to increase the weights of unique features. The experimental results demonstrate the outstanding performance of the proposed method in terms of the number of detected targets, accuracy rate and false alarm rate compared with baseline methods. In particular, the proposed method has a simple network structure and a lower computational cost which can be further promoted and implemented in actual engineering.