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Abstract Many studies have shown that drinking coffee and tea may be associated with the risk of hypertension and dementia. Limited research exists on their impact on dementia risk in hypertensive patients. This study aimed to determine the association between coffee and tea consumption and the risk of dementia development in hypertensive population by utilizing Cox proportional risk modeling with 453,913 participants from a UK biobank. Our findings reveal a J-shaped and U-shaped association between the risk of all-cause dementia and the consumption of coffee and tea respectively in hypertensive people. The hypertensive patients who drink 0.5–1 cup of coffee or 4–5 cups of tea per day have the lowest risk of dementia. A U-shaped relationship was observed between daily caffeine consumption and the risk of developing all-cause dementia and vascular dementia in the hypertensive population. Furthermore, the significant association between the amount of coffee and tea consumed and the risk of all-cause and vascular dementia were more likely to be found in hypertensive patients than in the non-hypertensive population.

Abstract Background Extensive evidence indicates that both lifestyle factors and air pollution are strongly associated with all-cause mortality. However, little studies in this field have integrated these two factors in order to examine their relationship with mortality and explore potential interactions. Methods A cohort of 271,075 participants from the UK Biobank underwent analysis. Lifestyles in terms of five modifiable factors, namely smoking, alcohol consumption, physical activity, diet, and sleep quality, were classified as unhealthy (0–1 score), general (2–3 score), and healthy (4–5 score). Air pollution, including particle matter with a diameter ≤ 2.5 μm (PM2.5), particulate matter with a diameter ≤ 10 μm (PM10), particulate matter with a diameter 2.5–10 μm (PM2.5−10), nitrogen dioxide (NO2), and nitrogen oxides (NOx), was divided into three levels (high, moderate, and low) using Latent Profile Analysis (LPA). Cox proportional hazard regression analysis was performed to examine the links between lifestyle, air pollution, and all-cause mortality before and after adjustment for potential confounders. Restricted cubic spline curves featuring three knots were incorporated to determine nonlinear relationships. The robustness of the findings was assessed via subgroup and sensitivity analyses. Results With unhealthy lifestyles have a significantly enhanced risk of death compared to people with general lifestyles (HR = 1.315, 95% CI, 1.277–1.355), while people with healthy lifestyles have a significantly lower risk of death (HR = 0.821, 95% CI, 0.785–0.858). Notably, the difference in risk between moderate air pollution and mortality risk remained insignificant (HR = 0.993, 95% CI, 0.945–1.044). High air pollution, on the other hand, was independently linked to increased mortality risk as compared to low air pollution (HR = 1.162, 95% CI, 1.124–1.201). The relationship between NOx, PM10, and PM2.5−10 and all-cause mortality was found to be nonlinear (p for nonlinearity

IntroductionOilseed flax (Linum usitatissimum L.) yields are phosphate (P) fertilizer-limited, especially in the temperate semiarid dryland regions of North China. However, there are limited studies on the effects of P-fertilizer inputs on plant growth and soil microorganisms in flax planting systems.MethodsTo address this gap, a field experiment was conducted with four treatments: no P addition and application of 40, 80, and 120 kg P ha-¹, respectively. The aim was to investigate the influence of various P fertilizer inputs on yield, plant dry matter, P use efficiency, as well as the population of soil arbuscular mycorrhizal fungi (AMF) and bacteria in dryland oilseed flax.ResultsOur results show that the P addition increased the dry matter, and the yield of oilseed increased by ~200% at 120 kg P ha-1 addition with inhibition on the growth of AMF hyphae. The moderate P supply (80 kg ha-1) was adequate for promoting P translocation, P use efficiency, and P recovery efficiency. Soil pH, available P, and available K significantly (p< 0.05) promoted the abundance of the dominant taxa (Acidobacteria_GP6, Sphingobacteria and Bacteroidetes). In addition, it is imperative to comprehend the mechanism of interaction between phosphorus-fertilizer inputs and microbiota in oilseed flax soil. DiscussionThis necessitates further research to quantify and optimize the moderate phosphorus supply, regulate soil microbes to ensure high phosphorus utilization, and ultimately establish a sustainable system for oilseed flax cultivation in the local area.

In data acquisition and transmission, hyperspectral images are inevitably corrupted by additive noises, making it challenging to accurately observe and recognize the materials on the surface of the Earth. However, scholars have been addicted to developing numerous complex methods for separable two-stage denoising and anomaly detection (AD) tasks over the past years, rarely paying attention to the real effect of noises for subsequent intelligent interpretation. To this date, we propose a hierarchical integration framework for hyperspectral simultaneous AD and denoising (HyADD). Joint AD and denoising are mutually integrated and their outputs in each iteration stimulate each other, breaking through the respective performance bottlenecks of the separable two-stage scheme. Inspired by spatial–spectral gradient domain-based constraint, HyADD removes additive noises and preserves advantageous image smoothness information to improve intermediate detection performances in the iteration loop. Conversely, with the assistance of the antinoise dictionary conduction and the subspace domain-based low-rankness, the identification of anomalies with different features from the background can provide effective feedback to the denoising process. The proposed algorithm is efficiently solved by the well-designed linearized alternating direction method of multipliers with an adaptive penalty. A comparison with the existing well-known AD algorithms via simulated and real-world experiments establishes the competitiveness of the proposed HyADD with state-of-the-art methods.

Extraction of vectorized building instances from remote sensing images has made significant progress in recent years. The extraction of vectorized building maps enables rapid, large-scale updates to geospatial databases. Recently, the contour-based methods have achieved amazing success because their learning goals are more compatible with vectorized polygons. However, existing contour-based approaches achieve contour updates only by learning the local features at each vertex, which can lead to a contour smoothing problem. Besides, these methods achieve classification via features at the center point, which hinders the receptive field at the instance level. Our primary motivation is to overcome existing limitations introduced by local modeling during contour regression while simultaneously improving the instance classification performance. In this article, we utilize instance-level features to guide the contour learning process. An instance-aware contour regression (IACR) module is designed to update the local contour features via cross attention on the instance-level features. Furthermore, based on the IACR module, we propose a novel vectorized building extraction framework BuildingVec, which interacts between the contour regression branch and instance classification branch through a well-designed cascade architecture. We also build a vectorized building dataset with fine-grained categories, named UBCv2 (vec), to benefit the study of vectorized extraction for fine-grained buildings. Experiments on the UBCv2 (vec) dataset demonstrate that BuildingVec achieves state-of-the-art performance compared to both mask-based methods and contour-based methods. When compared to other vectorized extraction methods on the Crowd AI dataset, BuildingVec also achieves a state-of-the-art performance with an $\text{AP}_{50}$ of 93.1%.

This article presents the scientific outcomes of the 2023 Data Fusion Contest (DFC23) organized by the Image Analysis and Data Fusion Technical Committee of the IEEE Geoscience and Remote Sensing Society. The contest consists of two tracks investigating the fusion of optical and synthetic aperture radar data for: 1) fine-grained roof type classification and 2) height estimation. During the development phase, 1000 people registered for the contest, while at the end 55 and 35 teams competed during the test phase in the two tracks, respectively. This article presents the methods and results obtained by the first and second-ranked teams of each track. In Track 1, both winning teams leveraged pretraining, modern network architectures, model ensembles, and measures to cope with the imbalanced class distribution. The solutions to Track 2 are more diverse and are characterized by modern multitask learning approaches. The data of this contest is openly available to the community for further research, development, and refinement of machine learning methods.

Porcine deltacoronavirus (PDCoV) is an emerging enteric pathogen that causes substantial economic losses in the swine industry worldwide. The PDCoV NS6 protein is an accessory protein that plays a pivotal role in the viral life cycle and immune evasion. However, the functions of NS6 and its role in PDCoV pathogenesis remain largely unknown. In this study, we prepared a monoclonal antibody (mAb) 5-A11 that specifically recognizes the PDCoV NS6 protein. The mAb 5-A11 exhibited high specificity for PDCoV, with no cross-reactivity with several major porcine pathogenic viruses. Furthermore, the epitope recognized by mAb 5-A11 was precisely mapped to residues 70EYGSIYGKDFI80 of the NS6 protein using Western blot analysis. Notably, this epitope is highly conserved among different PDCoV isolates. Substantial variations were observed when comparing this epitope with the corresponding regions in the NS6 proteins of other δ coronaviruses, suggesting potential differences in the structure, function, and antigenicity of their NS6 proteins. Our findings provide valuable tools and insights for further elucidating the functions of the NS6 protein and its role in PDCoV pathogenesis, as well as for developing diagnostic and therapeutic strategies against PDCoV infection.

Collaborative perception enhances onboard perceptual capability by integrating features from other platforms, effectively mitigating the compromised accuracy caused by a restricted observational range and vulnerability to interference. However, current implementations of collaborative perception overlook the prevalent issues of both limited and low-reliability communication, as well as misaligned observations in remote sensing. To address this problem, this article presents an innovative distributed collaborative perception network (DCP-Net) specifically designed for remote sensing applications. Firstly, a self-mutual information match module is proposed to identify collaboration opportunities and select suitable partners. This module prioritizes critical collaborative features and reduces redundant transmission for better adaptation to weak communication in remote sensing. Secondly, a related feature fusion module is devised to tackle the misalignment between local and collaborative features due to the multiangle observations, improving the quality of fused features for the downstream task. We conduct extensive experiments and visualization analyses using three semantic segmentation datasets, namely Potsdam, iSAID, and DFC23. The results demonstrate that DCP-Net outperforms the existing collaborative perception methods comprehensively, improving mIoU by 2.61% to 16.89% at the highest collaboration efficiency and achieving state-of-the-art performance.

Semantic segmentation with cross-domain adaptation in remote-sensing images (RSIs) is crucial and mitigates the expense of manually labeling target data. However, the performance of existing unsupervised domain adaptation (UDA) methods is still significantly impacted by domain bias, leading to a considerable gap compared to supervised trained models. To address this, our work focuses on semi-supervised domain adaptation, selecting a small subset of target annotations through active learning (AL) that maximize information to improve domain adaptation. Overall, we propose a novel active bidirectional self-training network (ABSNet) for cross-domain semantic segmentation in RSIs. ABSNet consists of two sub-stages: a multi-prototype active region selection (MARS) stage and a source-weighted class-balanced self-training (SCBS) stage. The MARS approach captures the diversity in labeled source data by introducing multi-prototype density estimation based on Gaussian mixture models. We then measure inter-domain similarity to select complementary and representative target samples. Through fine-tuning with the selected active samples, we propose an enhanced self-training strategy SCBS, designed for weighted training on source data, aiming to avoid the negative effects of interfering samples. We conduct extensive experiments on the LoveDA and ISPRS datasets to validate the superiority of our method over existing state-of-the-art domain-adaptive semantic segmentation methods.

Synthetic aperture radar (SAR) ship detection and classification has gained unprecedented attention due to its important role in maritime transportation. Many deep learning-based detectors and classifiers have been successfully applied and achieved great progress. However, ships in SAR images present discrete and multi-centric features, and their scattering characteristics and edge information are sensitive to variations in target attitude angles (TAAs). These factors pose challenges for existing methods to obtain satisfactory results. To address these challenges, a novel target attitude angle-guided network (TAG-Net) is proposed in this article. The core idea of TAG-Net is to leverage TAA information as guidance and use an adaptive feature-level fusion strategy to dynamically learn more representative features that can handle the target imaging diversity caused by TAA. This is achieved through a TAA-aware feature modulation (TAFM) module. It uses the TAA information and foreground information as prior knowledge and establishes the relationship between the ship scattering characteristics and TAA information. This enables a reduction in the intra-class variability and highlights ship targets. Additionally, considering the different requirements of the detection and classification tasks for the scattering information, we propose a layer-wise attention-based task decoupling detection head (LATD). Unlike general deep learning methods that use shared features for both detection and classification tasks, LATD extracts multi-level features and uses layer attention to achieve feature decoupling and select the most suitable features for each task. Finally, we introduce a novel salient-enhanced feature balance module (SFB) to provide richer semantic information and capture the global context to highlight ships in complex scenes, effectively reducing the impact of background noise. A large-scale ship detection dataset (LSSDD+) is used to verify the effectiveness of TAG-Net, and our method achieves state-of-the-art performance.

Although Microphysogobio tungtingensis (Nichols, 1926) has been treated valid since it was described, its morphology remains vague, especially when comparing it with another similar species, M. elongatus (Yao & Yang, 1977). In this study, the types of both species were examined and also compared with several lots of specimens from a wide geographical range: there is no significant difference in morphology between them. Additionally, molecular evidence supported by mitochondrial gene sequence also showed low genetic distance in between. Thus, it is suggested that M. elongatus is a junior synonym of M. tungtingensis. While revising these two species, a new species, Microphysogobio punctatus sp. nov., was discovered that has a similar distribution with them both. However, it can be distinguished from its congeners by having a globular or oval shaped posterior air-bladder chamber which length 58.6%–82.8% of eye diameter; a narrow upper jaw cutting edge which less than half mouth width; a slender caudal peduncle with depth 34.6%–48.5% of length; and a six-branched-ray anal fin. This new species also has numerous small black spots on all fins which is also unique. The new species is morphologically and molecularly close to M. bicolor (Nichols, 1930).

Abstract In this paper, we construct a Dirac star model composed of $$|\kappa |$$ | κ | pairs of spinor fields. The azimuthal harmonic indices m of these spinor fields are half-integers, and they satisfy $$-(|\kappa |-\frac{1}{2})\le m \le |\kappa |-\frac{1}{2}$$ - ( | κ | - 1 2 ) ≤ m ≤ | κ | - 1 2 . When $$\kappa =1$$ κ = 1 , it corresponds to the conventional Dirac star model, formed by two spinor fields with $$m=\frac{1}{2}$$ m = 1 2 and $$m=-\frac{1}{2}$$ m = - 1 2 . When $$|\kappa |>1$$ | κ | > 1 , among these $$|\kappa |$$ | κ | pairs of spinor fields, there exist spinor fields with azimuthal harmonic indices $$m>\frac{1}{2}$$ m > 1 2 , and all spinor fields still conform to the same Dirac field equation. Different families of solutions are distinguished by the value of $$\kappa $$ κ , so we named these solutions $$\kappa $$ κ -Dirac stars. We obtain solutions for $$\kappa =\pm 1,\pm 2,\pm 3,\pm 4,\pm 5,\pm 6$$ κ = ± 1 , ± 2 , ± 3 , ± 4 , ± 5 , ± 6 by using numerical methods. Additionally, we compute their ADM mass M, Noether charge Q, and binding energy E, and illustrate how these quantities change with the spinor field’s frequency $$\omega $$ ω for different $$\kappa $$ κ . We observe significant differences between solutions for $$|\kappa |>1$$ | κ | > 1 and the $$|\kappa |=1$$ | κ | = 1 case. Furthermore, we provide the energy density distribution of the Dirac stars, wherein for $$|\kappa |>1$$ | κ | > 1 scenarios, the Dirac stars exhibit a spherical shell-like structure. Moreover, we employ three-dimensional diagrams to intuitively depict how $$\kappa $$ κ influences the combination of spinor fields to form a spherically symmetric configuration.

Abstract Background Diosgenin is an important steroidal precursor renowned for its diverse medicinal uses. It is predominantly sourced from Dioscorea species, particularly Dioscorea zingiberensis. Dioscorea zingiberensis has an ability to accumulate 2–16% diosgenin in its rhizomes. In this study, a diverse population of 180 D. zingiberensis accessions was used to evaluate the genomic regions associated with diosgenin biosynthesis by the genome wide association study approach (GWAS). Results The whole population was characterized for diosgenin contents from tubers by gas chromatography mass spectrometry. The individuals were genotyped by the genotyping-by-sequencing approach and 10,000 high-quality SNP markers were extracted for the GWAS. The highest significant marker-trait-association was observed as an SNP transversion (G to T) on chromosome 10, with 64% phenotypic variance explained. The SNP was located in the promoter region of CYP94D144 which is a member of P450 gene family involved in the independent biosynthesis of diosgenin from cholesterol. The transcription factor (TF) binding site enrichment analysis of the promoter region of CYP94D144 revealed NAC TF as a potential regulator. The results were further validated through expression profiling by qRT-PCR, and the comparison of high and low diosgenin producing hybrids obtained from a bi-parental population. Conclusions This study not only enhanced the understanding of the genetic basis of diosgenin biosynthesis but also serves as a valuable reference for future genomic investigations on CYP94D144, with the aim of augmenting diosgenin production in yam tubers.