Search Results (11)

Understanding spatiotemporal mismatches between urban expansion and population dynamics is essential for guiding sustainable development in rapidly urbanizing regions. Using multi-source nighttime light (NTL) images and global settlement layers, this study investigates the settlement growth pattern and potential spatiotemporal mismatch with population distribution in Guangdong, China, from 1995 to 2019 at a 5-year interval. Specifically, population spatialization in urban and rural areas is separately mapped by adopting a population-based thresholding method, achieving strong agreement with the census record. Our analysis reveals distinct expansion patterns and mismatch conditions across Guangdong’s Core, Belt, and District subzones. The Core and District subzones primarily experienced infilling and edge-expansion urban growth, while the Belt subzone exhibited more dispersed spatial patterns. Notably, only 5 of 21 prefectures exhibited faster population growth than urban expansion, likely due to sustained migration driven by economic opportunities and advanced urbanization. Quantitatively, both urban expansion and population growth followed a Core, Belt, District order. Spatially, population-dominated areas were primarily clustered within 10 km of urban centers, while the District subzone extensively displayed overfilled settlements, indicating low-efficient land use. Temporally, urban growth relative to population in the Core subzone turned from slower pre-2000 to faster post-2000, followed by gradual deceleration, while the Belt subzone maintained balanced growth throughout the study period. The District subzone sustained faster urban growth from 2000 to 2019. Findings of the study provide an important reference for scientific urban planning and sustainable regional development, not only in Guangzhou but other rapidly urbanizing regions globally. Full article

Public health crises triggered by viral infections pose severe threats to individual health and disrupt global socioeconomic systems. Against the backdrop of global pandemics caused by highly infectious diseases such as COVID-19 and Ebola virus disease (EVD), the development of innovative prevention and treatment strategies has become a strategic priority in the field of biomedicine. Neutralizing antibodies, as biological agents, are increasingly recognized for their potential in infectious disease control. Among these, nanobodies (Nbs) derived from camelid heavy-chain antibodies exhibit remarkable technical advantages due to their unique structural features. Compared to traditional neutralizing antibodies, nanobodies offer significant cost-effectiveness in production and enable versatile administration routes (e.g., subcutaneous injection, oral delivery, or aerosol inhalation), making them particularly suitable for respiratory infection control and resource-limited settings. Furthermore, engineered modification strategies—including multivalent constructs, multi-epitope recognition designs, and fragment crystallizable (Fc) domain fusion—effectively enhance their neutralizing activity and suppress viral immune escape mechanisms. Breakthroughs have been achieved in combating pathogens such as the Ebola virus and SARS-CoV-2, with mechanisms involving the blockade of virus–host interactions, induction of viral particle disintegration, and enhancement of immune responses. This review comprehensively discusses the structural characteristics, high-throughput screening technologies, and engineering strategies of nanobodies, providing theoretical foundations for the development of novel antiviral therapeutics. These advances hold strategic significance for addressing emerging and re-emerging infectious diseases. Full article

Exosomes are nanoscale vesicles secreted by cells that play vital regulatory roles in intercellular communication and immune responses. Listeria monocytogenes (L. Monocytogenes, LM) is a notable Gram-positive intracellular parasitic bacterium that infects humans and diverse animal species. However, the specific biological function of exosomes secreted by macrophages during L. Monocytogenes infection (hereafter EXO-LM) remains elusive. Here, we discovered that EXO-LM stimulated the secretion of inflammation-associated cytokines by macrophages, facilitating the intracellular survival of L. monocytogenes within macrophages. Transcriptomic analysis shows that EXO-LM significantly upregulates immune recognition and inflammation-related signaling pathways in macrophages. Furthermore, a ceRNA regulatory network comprising exosomal ncRNAs and macrophage RNAs was constructed through EXO-LM transcriptome sequencing. Utilizing bioinformatics and dual-luciferase reporter assays, we identified two potential binding sites between lncRNA Rpl13a-213 and miR-132-3p. Cell transfection experiments demonstrated that Rpl13a-213 overexpression augmented pro-inflammatory cytokine expression in macrophages, in contrast to the suppression by miR-132-3p overexpression. The decrease in Rpl13a-213 upon EXO-LM stimulation enhances miR-132-3p expression, dampening the inflammatory response in macrophages and aiding L. monocytogenes intracellular survival. This study unveils the immunomodulatory function of exosomal ncRNAs originating from macrophages, which provides fresh perspectives into the mechanisms underlying macrophage inflammatory response regulation by L. monocytogenes-infected cell-derived exosomes. Full article

Deep-learning-based ship detection methods in Synthetic Aperture Radar (SAR) imagery are a current research hotspot. However, these methods rely on high-quality images as input, and in practical applications, SAR images are interfered with by speckle noise, leading to a decrease in image quality and thus affecting detection accuracy. To address this problem, we propose a unified framework for ship detection that incorporates a despeckling module into the object detection network. This integration is designed to enhance the detection performance, even with low-quality SAR images that are affected by speckle noise. Secondly, we propose a Multi-Scale Window Swin Transformer module. This module is adept at improving image quality by effectively capturing both global and local features of the SAR images. Additionally, recognizing the challenges associated with the scarcity of labeled data in practical scenarios, we employ an unlabeled distillation learning method to train our despeckling module. This technique avoids the need for extensive manual labeling and making efficient use of unlabeled data. We have tested the robustness of our method using public SAR datasets, including SSDD and HRSID, as well as a newly constructed dataset, the RSSDD. The results demonstrate that our method not only achieves a state-of-the-art performance but also excels in conditions with low signal-to-noise ratios. Full article

This study conducted transcriptome sequencing of goat-mammary-gland tissue at the late lactation (LL), dry period (DP), and late gestation (LG) stages to reveal the expression characteristics and molecular functions of circRNAs during mammary involution. A total of 11,756 circRNAs were identified in this study, of which 2528 circRNAs were expressed in all three stages. The number of exonic circRNAs was the largest, and the least identified circRNAs were antisense circRNAs. circRNA source gene analysis found that 9282 circRNAs were derived from 3889 genes, and 127 circRNAs’ source genes were unknown. Gene Ontology (GO) terms, such as histone modification, regulation of GTPase activity, and establishment or maintenance of cell polarity, were significantly enriched (FDR < 0.05), which indicates the functional diversity of circRNAs’ source genes. A total of 218 differentially expressed circRNAs were identified during the non-lactation period. The number of specifically expressed circRNAs was the highest in the DP and the lowest in LL stages. These indicated temporal specificity of circRNA expression in mammary gland tissues at different developmental stages. In addition, this study also constructed circRNA–miRNA–mRNA competitive endogenous RNA (ceRNA) regulatory networks related to mammary development, immunity, substance metabolism, and apoptosis. These findings help understand the regulatory role of circRNAs in mammary cell involution and remodeling. Full article

To understand how genes precisely regulate lactation physiological activity and the molecular genetic mechanisms underlying mammary gland involution, this study investigated the transcriptome characteristics of goat mammary gland tissues at the late gestation (LG), early lactation (EL), peak lactation (PL), late lactation (LL), dry period (DP), and involution (IN) stages. A total of 13,083 differentially expressed transcripts were identified by mutual comparison of mammary gland tissues at six developmental stages. Genes related to cell growth, apoptosis, immunity, nutrient transport, synthesis, and metabolism make adaptive transcriptional changes to meet the needs of mammary lactation. Notably, platelet derived growth factor receptor beta (PDGFRB) was screened as a hub gene of the mammary gland developmental network, which is highly expressed during the DP and IN. Overexpression of PDGFRB in vitro could slow down the G1/S phase arrest of goat mammary epithelial cell cycle and promote cell proliferation by regulating the PI3K/Akt signaling pathway. In addition, PDGFRB overexpression can also affect the expression of genes related to apoptosis, matrix metalloproteinase family, and vascular development, which is beneficial to the remodeling of mammary gland tissue during involution. These findings provide new insights into the molecular mechanisms involved in lactation and mammary gland involution. Full article

As a vital metabolic and immune organ in animals, the liver plays an important role in protein synthesis, detoxification, metabolism, and immune defense. The primary research purpose of this study was to reveal the effect of breast-feeding, weaning transition, and weaning on the gene expression profile in the goat kid liver and to elucidate the transcriptome-level signatures associated with liver metabolic adaptation. Therefore, transcriptome sequencing was performed on liver tissues, which was collected at 1 day (D1), 2 weeks (W2), 4 weeks (W4), 8 weeks (W8), and 12 weeks (W12) after birth in Laiwu black goats at five different time-points, with five goats at each time point. From 25 libraries, a total of 37497 mRNAs were found to be expressed in goat kid livers, and 1271 genes were differentially expressed between at least two of the five time points. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses revealed that these genes were annotated as an extracellular region fraction, exhibiting monooxygenase activity, positive regulation of T cell activation, mitotic spindle mid-region assembly, cytokinesis, cytoskeleton-dependent cytokinesis, regulation of cytokinesis, regulation of lymphocyte proliferation, and so on. In addition, it mainly deals with metabolism, endocrine, cell proliferation and apoptosis, and immune processes. Finally, a gene regulatory network was constructed, and a total of 14 key genes were screened, which were mainly enriched for cell growth and development, endocrine, immune, and signal transduction-related pathways. Our results provide new information on the molecular mechanisms and pathways involved in liver development, metabolism, and immunity of goats. Full article

Surface albedo, as a key parameter determining the partition of solar radiation at the Earth’s surface, has been developed into a satellite-based product from various Earth observation systems to serve numerous global or regional applications. Studies point out that apparent uncertainty can be introduced into albedo retrieval without consideration of surface anisotropy, which is a challenge to albedo estimation especially from observations with fewer angular samplings. Researchers have begun to introduce smoothed anisotropy prior knowledge into albedo estimation to improve the inversion efficiency, or for the scenario of observations with signal or poor angular sampling. Thus, it is necessary to further understand the potential influence of smoothed anisotropy features adopted in albedo estimation. We investigated the albedo variation induced by BRDF smoothing at both temporal and spatial scales over six typical landscapes in North America using MODIS standard anisotropy products with high quality BRDF inversed from multi-angle observations in 500 m and 5.6 km spatial resolutions. Components of selected typical landscapes were assessed with the confidence of the MCD12 land cover product and 30 m CDL (cropland data layer) classification maps followed by an evaluation of spatial heterogeneity in 30 m scale through the semi-variogram model. High quality BRDF of MODIS standard anisotropy products were smoothed in multi-temporal scales of 8 days, 16 days, and 32 days, and in multi-spatial scales from 500 m to 5.6 km. The induced relative and absolute albedo differences were estimated using the RossThick-LiSparseR model and BRDFs smoothed before and after spatiotemporal smoothing. Our results show that albedo estimated using BRDFs smoothed temporally from daily to monthly over each scenario exhibits relative differences of 11.3%, 12.5%, and 27.2% and detectable absolute differences of 0.025, 0.012, and 0.013, respectively, in MODIS near-infrared (0.7–5.0 µm), short-wave (0.3–5.0 µm), and visible (0.3–0.7 µm) broad bands. When BRDFs of investigated landscapes are smoothed from 500 m to 5.6 km, variations of estimated albedo can achieve up to 36.5%, 37.1%, and 94.7% on relative difference and absolute difference of 0.037, 0.024, and 0.018, respectively, in near-infrared (0.7–5.0 µm), short wave (0.3–5.0 µm), and visible (0.3–0.7 µm) broad bands. In addition, albedo differences caused by temporal smoothing show apparent seasonal characteristic that the differences are significantly higher in spring and summer than those in autumn and winter, while albedo differences induced by spatial smoothing exhibit a noticeable relationship with sill values of a fitted semi-variogram marked by a correlation coefficient of 0.8876. Both relative and absolute albedo differences induced by BRDF smoothing are demonstrated to be captured, thus, it is necessary to avoid the smoothing process in quantitative remote sensing communities, especially when immediate anisotropy retrievals are available at the required spatiotemporal scale. Full article

Water resources are closely linked to human productivity and life. Owing to the deteriorating water resources environment, accurate and rapid determination of the main water quality parameters has become a current research hotspot. Ultraviolet-visible (UV-Vis) spectroscopy offers an effective tool for qualitative analysis and quantitative detection of contaminants in a water environment. In this review, the principle and application of UV-Vis technology in water quality detection were studied. The principle of UV-Vis spectroscopy for detecting water quality parameters and the method of modeling and analysis of spectral data were presented. Various UV-Vis technologies for water quality detection were reviewed according to the types of pollutants, such as chemical oxygen demand, heavy metal ions, nitrate nitrogen, and dissolved organic carbon. Finally, the future development of UV-Vis spectroscopy for the determination of water quality was discussed. Full article

In this study, a hyperspectral imaging system of 866.4–1701.0 nm, combined with a variety of spectral processing methods were adopted to identify shrimp freshness. To gain the optimal model combination, three preprocessing methods (Savitzky-Golay first derivative (SG1), multivariate scatter correction (MSC), and standard normal variate (SNV)), three characteristic wavelength extraction algorithms (random frog algorithm (RFA), uninformative variables elimination (UVE), and competitive adaptive reweighted sampling (CARS)), and four discriminant models (partial least squares discrimination analysis (PLS-DA), least squares support vector machine (LSSVM), random forest (RF), and extreme learning machine (ELM)) were employed for experimental study. First of all, due to the full wavelength modeling analysis, three preprocessing methods were utilized to preprocess the original spectral data. The analysis showed that the spectral data processed by the SNV method had the best performance among the four discriminant models. Secondly, due to the characteristic wavelength modeling analysis, three characteristic wavelength extraction algorithms were utilized to extract the characteristic wavelength of the SNV-processed spectral data. It was found that the CARS algorithm achieved the best performance among the three characteristic wavelength extraction algorithms, and the combining adoption of the ELM model and different characteristic wavelength extraction algorithms obtained the best results. Therefore, the model based on SNV-CARS-ELM obtained the best performance and was elected as the optimal model. Lastly, for accurately and explicitly displaying the refrigeration days of shrimps, the original hyperspectral images of shrimps were substituted into the SNV-CARS-ELM model, thus obtaining the general classification accuracy of 97.92%, and the object-wise method was used to visualize the classification results. As a result, the method proposed in this study can effectively detect the freshness of shrimps. Full article

Multi-label text classification refers to a text divided into multiple categories simultaneously, which corresponds to a text associated with multiple topics in the real world. The feature space generated by text data has the characteristics of high dimensionality and sparsity. Feature selection is an efficient technology that removes useless and redundant features, reduces the dimension of the feature space, and avoids dimension disaster. A feature selection method for multi-label text based on feature importance is proposed in this paper. Firstly, multi-label texts are transformed into single-label texts using the label assignment method. Secondly, the importance of each feature is calculated using the method based on Category Contribution (CC). Finally, features with higher importance are selected to construct the feature space. In the proposed method, the feature importance is calculated from the perspective of the category, which ensures the selected features have strong category discrimination ability. Specifically, the contributions of the features to each category from two aspects of inter-category and intra-category are calculated, then the importance of the features is obtained with the combination of them. The proposed method is tested on six public data sets and the experimental results are good, which demonstrates the effectiveness of the proposed method. Full article