Search Results (41)

Space weather monitoring depends critically on passive sensor systems that detect and measure natural solar and geospace emissions without transmitting radio frequency energy. These include riometers, solar radio monitors, interplanetary scintillation detectors, GNSS-based ionospheric sensors, and broadband solar spectrographs that enable the provision of critical data required to forecast geomagnetic storms, protect critical infrastructures, and support aviation services, satellite operations, and defense services. However, with the increasing proliferation of radiocommunication technologies such as 5G/6G networks, dense HF/VHF/UHF deployments, and large constellations of low-Earth-orbit (LEO) satellites, the interference threat to these exceptionally sensitive receivers has grown. Most of these operate near the thermal noise floor and thus require strict protection criteria to ensure continuity of data. This review and perspective article provides a cross-disciplinary synthesis of scientific requirements, documented RFI case studies, and ongoing regulatory developments related to spectrum protection for passive space weather sensors. It systematically integrates perspectives on physical, technical, and regulatory aspects that are typically addressed separately in the literature. The article reviews the operating principles of major sensor classes and analyzes documented RFI cases affecting GNSS, riometers, CALLISTO, BINGO, and systems impacted by LEO satellite emissions, drawing from existing reports and regulatory submissions. Building on this evidence base, the work comparatively evaluates regulatory methods under consideration for WRC-27 shows that hybrid approaches combining primary allocations in core observation bands with secondary status and coordination procedures in adjacent bands offer the most viable path forward. This synthesis contextualizes and analyzes how technical protection criteria can be integrated with existing and evolving regulatory instruments to inform spectrum governance. The study concludes that without coordinated international spectrum management incorporating explicit protection thresholds and registration procedures, the long-term viability of space weather monitoring infrastructure faces significant risk in an increasingly congested radio frequency environment. Full article

Background: The characteristics and prognosis of HIV-associated Kaposi sarcoma (KS) among people living with HIV (PLWH), and their association with HHV-8 viral load are not well understood in Japan. Methods: We conducted a retrospective study of PLWH diagnosed with KS at Tokyo Medical University from 2000 to 2023. Results: Seventy cases of KS were identified; HHV-8 viral load data were available for twenty-three of these cases. The median age was 43 years (interquartile range [IQR], 11 years). The median HIV viral load at diagnosis was 150,000 copies/mL (IQR, 560,000 copies/mL). The median CD4 count was 76.0/μL (IQR, 157/μL). Lesions other than those of the skin were observed in the gastrointestinal tract (nine cases, 39.1%), oropharynx (three cases, 13.0%), and bronchial/lung (two cases, 8.7%). The median HHV-8 viral load was 0.0 copies/10⁶ WBC (IQR, 1500 copies/10⁶ WBC). Among the nine deceased PLWH, KS inflammatory cytokine syndrome (KICS) was diagnosed in five PLWH. Older age (≥50 years) and a high HHV-8 viral load (>615 copies/10⁶ WBCs) were significantly associated with worse survival. Conclusion: A high HHV-8 viral load may be a risk factor for mortality in PLWH with KS. Notably, all PLWH diagnosed with KICS in this study died, underscoring the poor prognosis associated with this condition. Full article

Oat × maize addition lines (OMAs) are plants of oat (Avena sativa L.) obtained by wide crossing with maize (Zea mays L.) that retained one or more maize chromosomes in the oat genome, which can result in morphological and physiological changes. The aim of the study was to determine the relationship between phenolics, pigments, sugars, and yield components in 14 OMAs and oat cv. Bingo under soil drought. The plants were sown in pots in a vegetation tunnel. The pots were watered to the level of 70% field water capacity (FWC) and then drought treated to 20% FWC for 2 weeks. Analysis of variance (ANOVA) showed that genotype and treatment significantly influenced the measured parameters. Out of 14 OMAs, lines 9 and 78b showed the highest grain weight and number, with the least amount of biomass loss under drought. These OMAs were the only two to equal or surpass the oat cv. Bingo under drought and control conditions. On average, soil drought caused decrease in biomass and the number and mass of grains (30%, 44%, 46%, respectively). Soil drought increased the amount of sugars by 15% and phenolics by 9% but decreased pigment contents by 8%. According to Pearson’s correlation coefficients, fifteen pairs of traits were positively and statistically significantly correlated in control and drought conditions. Significant relationships were found between the yield components and biochemical parameters on the fourteenth day of drought. A positive correlation occurred between the number and weight of kernels and the content of soluble sugars, chlorophyll a, b, and the sum of a and b. A negative correlation was found between all analyzed yield components and the content of phenolics. The results suggest the possibility of using such biochemical parameters as a quick physiological indicator of plant tolerance to soil drought. Variation in studied OMA lines reveals substantial differences in drought response, offering promising opportunities for targeted selection and breeding strategies. Full article

This paper proposes a multi-party verifiably collaborative system for encrypting the nonlinear and the non-stationary biomedical signals captured by biomedical sensors via the singular spectrum analysis (SSA)-based chaotic networks. In particular, the raw signals are first decomposed into the multiple components by the SSA. Then, these decomposed components are fed into the chaotic filter bank networks for performing the encryption. To perform the multi-party verifiably collaborative encryption, the window length of the SSA and the total number of the layers in the chaotic network are flexibly designed to match the total number of the collaborators. The computer numerical simulation results show that our proposed system achieves a good encryption performance. Full article

For four-channel photoplethysmograms (PPGs), this paper employs quaternion-valued medians as features for performing non-invasive blood glucose estimation. However, as the PPGs are contaminated by noise, the quaternion-valued medians are also contaminated by noise. To address this issue, principal component analysis (PCA) is employed for performing the denoising. In particular, the covariance matrix of the four-channel PPGs is computed and the eigen vectors of the covariance matrix are found. Then, the quaternion-valued medians of the four-channel PPGs are found and these quaternion-valued medians are represented as the four-channel real-valued vectors. By applying the PCA to these four-channel real-valued vectors and reconstructing the denoised four-dimensional real-valued vectors, these four-dimensional real-valued vectors are denoised. Next, these denoised four-dimensional real-valued vectors are represented as the denoised quaternion-valued medians. Compared to the traditional denoising methods and the traditional feature extraction methods that are performed in the individual channels, the quaternion-valued medians and the PCA are computed via fusing all of these four-channel PPGs together. Hence, the hidden relationships among these four channels of the PPGs are exploited. Finally, the random forest is used to estimate the blood glucose levels (BGLs). Our proposed PCA-based quaternion-valued medians are compared to the median of each channel of the PPGs and other features such as the time-domain features and the frequency-domain features. Here, the effectiveness and robustness of our proposed method is demonstrated using two datasets. The computer numerical simulation results indicate that our proposed PCA-based quaternion-valued medians outperform the existing quaternion-valued medians and the other features for performing non-invasive blood glucose estimation. Full article

Contrastive learning has emerged as a dominant paradigm for understanding 3D open-world environments, particularly in the realm of multi-modalities. However, due to the nature of self-supervised learning and the limited size of 3D datasets, pre-trained models in the 3D point cloud domain often suffer from overfitting in downstream tasks, especially in zero-shot classification. To tackle this problem, we design a module to mine and enhance hard negatives from datasets, which are useful to improve the discrimination of models. This module could be seamlessly integrated into cross-modal contrastive learning frameworks, addressing the overfitting issue by enhancing the mined hard negatives during the process of training. This module consists of two key components: mining and enhancing. In the process of mining, we identify hard negative samples by examining similarity relationships between vision–vision and vision–text modalities, locating hard negative pairs within the visual domain. In the process of enhancing, we compute weighting coefficients via the similarity differences of these mined hard negatives. By enhancing the mined hard negatives while leaving others unchanged, we improve the overall performance and discrimination of models. A series of experiments demonstrate that our module can be easily incorporated into various contrastive learning frameworks, leading to improved model performance in both zero-shot and few-shot tasks. Full article

Long-term treatment with bisphosphonates is accompanied by an increased risk of medication-related osteonecrosis of the jaw (MRONJ). Currently, no clinically useful biomarkers for the predictive diagnosis of MRONJ are available. To investigate the potential key proteins involved in the pathogenesis of MRONJ, a proteomic LC-MS/MS analysis of saliva was performed. Differentially expressed proteins (DEPs) were analyzed using BiNGO, ClueGO, cytoHubba, MCODE, KEGG, and ReactomeFI software packages using Cytoscape platforms. In total, 1545 DEPs were identified, including 43 up- and 11 down-regulated with a 1.5-fold cut-off value and adj. p-value < 0.05. The analysis provided a panel of hub genes, including APOA2, APOB, APOC2, APOC3, APOE, APOM, C4B, C4BPA, C9, FGG, GC, HP, HRG, LPA, SAA2-SAA4, and SERPIND1. The most prevalent terms in GO of the biological process were macromolecular complex remodeling, protein–lipid complex remodeling, and plasma lipoprotein particle remodeling. DEPs were mainly involved in signaling pathways associated with lipoproteins, the innate immune system, complement, and coagulation cascades. The current investigation advanced our knowledge of the molecular mechanisms underlying MRONJ. In particular, the research identified the principal salivary proteins that are implicated in the onset and progression of this condition. Full article

The integration of Synthetic Aperture Radar (SAR) imaging technology with deep neural networks has experienced significant advancements in recent years. Yet, the scarcity of high-quality samples and the difficulty of extracting prior information from SAR data have experienced limited progress in this domain. This study introduces an innovative sparse SAR imaging approach using a self-supervised non-local asymmetric pixel-shuffle blind spot network. This strategy enables the network to be trained without labeled samples, thus solving the problem of the scarcity of high-quality samples. Through asymmetric pixel-shuffle downsampling (AP) operation, the spatial correlation between pixels is broken so that the blind spot network can adapt to the actual scene. The network also incorporates a non-local module (NLM) into its blind spot architecture, enhancing its capability to analyze a broader range of information and extract more comprehensive prior knowledge from SAR data. Subsequently, Plug and Play (PnP) technology is used to integrate the trained network into the sparse SAR imaging model to solve the regularization term problem. The optimization of the inverse problem is achieved through the Alternating Direction Method of Multipliers (ADMM) algorithm. The experimental results of the unlabeled samples demonstrate that our method significantly outperforms traditional techniques in reconstructing images across various regions. Full article

Oceanic targets, including ripples, islands, vessels, and coastlines, display distinct sparse characteristics, rendering the ocean a significant arena for sparse Synthetic Aperture Radar (SAR) imaging rooted in sparse signal processing. Deep neural networks (DNNs), a current research emphasis, have, when integrated with sparse SAR, attracted notable attention for their exceptional imaging capabilities and high computational efficiency. Yet, the efficiency of traditional unfolding techniques is impeded by their architecturally inefficient design, which curtails their information transmission capacity and consequently detracts from the quality of reconstruction. This paper unveils a novel Memory-Augmented Deep Unfolding Network (MADUN) for SAR imaging in marine environments. Our methodology harnesses the synergies between deep learning and algorithmic unfolding, enhanced with a memory component, to elevate SAR imaging’s computational precision. At the heart of our investigation is the incorporation of High-Throughput Short-Term Memory (HSM) and Cross-Stage Long-Term Memory (CLM) within the MADUN framework, ensuring robust information flow across unfolding stages and solidifying the foundation for deep, long-term informational correlations. Our experimental results demonstrate that our strategy significantly surpasses existing methods in enhancing the reconstruction of sparse marine scenes. Full article

The integration of deep neural networks into sparse synthetic aperture radar (SAR) imaging is explored to enhance SAR imaging performance and reduce the system’s sampling rate. However, the scarcity of training samples and mismatches between the training data and the SAR system pose significant challenges to the method’s further development. In this paper, we propose a novel SAR imaging approach based on deep image prior powered by RED (DeepRED), enabling unsupervised SAR imaging without the need for additional training data. Initially, DeepRED is introduced as the regularization technique within the sparse SAR imaging model. Subsequently, variable splitting and the alternating direction method of multipliers (ADMM) are employed to solve the imaging model, alternately updating the magnitude and phase of the SAR image. Additionally, the SAR echo simulation operator is utilized as an observation model to enhance computational efficiency. Through simulations and real data experiments, we demonstrate that our method maintains imaging quality and system downsampling rate on par with deep-neural-network-based sparse SAR imaging but without the requirement for training data. Full article

The objective was to evaluate the current evidence regarding the etiology of medication-related osteonecrosis of the jaw (MRONJ). This study systematically reviewed the literature by searching PubMed, Web of Science, and ProQuest databases for genes, proteins, and microRNAs associated with MRONJ from the earliest records through April 2023. Conference abstracts, letters, review articles, non-human studies, and non-English publications were excluded. Twelve studies meeting the inclusion criteria involving exposure of human oral mucosa, blood, serum, saliva, or adjacent bone or periodontium to anti-resorptive or anti-angiogenic agents were analyzed. The Cochrane Collaboration risk assessment tool was used to assess the quality of the studies. A total of 824 differentially expressed genes/proteins (DEGs) and 22 microRNAs were extracted for further bioinformatic analysis using Cytoscape, STRING, BiNGO, cytoHubba, MCODE, and ReactomeFI software packages and web-based platforms: DIANA mirPath, OmicsNet, and miRNet tools. The analysis yielded an interactome consisting of 17 hub genes and hsa-mir-16-1, hsa-mir-21, hsa-mir-23a, hsa-mir-145, hsa-mir-186, hsa-mir-221, and hsa-mir-424. A dominance of cytokine pathways was observed in both the cluster of hub DEGs and the interactome of hub genes with dysregulated miRNAs. In conclusion, a panel of genes, miRNAs, and related pathways were found, which is a step toward understanding the complexity of the disease. Full article

With the advent of autonomous vehicle applications, the importance of LiDAR point cloud 3D object detection cannot be overstated. Recent studies have demonstrated that methods for aggregating features from voxels can accurately and efficiently detect objects in large, complex 3D detection scenes. Nevertheless, most of these methods do not filter background points well and have inferior detection performance for small objects. To ameliorate this issue, this paper proposes an Attention-based and Multiscale Feature Fusion Network (AMFF-Net), which utilizes a Dual-Attention Voxel Feature Extractor (DA-VFE) and a Multi-scale Feature Fusion (MFF) Module to improve the precision and efficiency of 3D object detection. The DA-VFE considers pointwise and channelwise attention and integrates them into the Voxel Feature Extractor (VFE) to enhance key point cloud information in voxels and refine more-representative voxel features. The MFF Module consists of self-calibrated convolutions, a residual structure, and a coordinate attention mechanism, which acts as a 2D Backbone to expand the receptive domain and capture more contextual information, thus better capturing small object locations, enhancing the feature-extraction capability of the network and reducing the computational overhead. We performed evaluations of the proposed model on the nuScenes dataset with a large number of driving scenarios. The experimental results showed that the AMFF-Net achieved 62.8% in the mAP, which significantly boosted the performance of small object detection compared to the baseline network and significantly reduced the computational overhead, while the inference speed remained essentially the same. AMFF-Net also achieved advanced performance on the KITTI dataset. Full article

A significant genetic involvement has been known for decades to exist in adolescent idiopathic scoliosis (AIS), a spine deformity affecting 1–3% of the world population. However, though biomechanical and endocrinological theories have emerged, no clear pathophysiological explanation has been found. Data from the whole-exome sequencing performed on 113 individuals in 19 multi-generational families with AIS have been filtered and analyzed via interaction pathways and functional category analysis (Varaft, Bingo and Panther). The subsequent list of 2566 variants has been compared to the variants already described in the literature, with an 18% match rate. The familial analysis in two families reveals mutations in the BICD2 gene, supporting the involvement of the muscular system in AIS etiology. The cellular component analysis revealed significant enrichment in myosin-related and neuronal activity-related categories. All together, these results reinforce the suspected role of the neuronal and muscular systems, highlighting the calmodulin pathway and suggesting a role of DNA-binding activities in AIS physiopathology. Full article

Complex diseases are associated with the effects of multiple genes, proteins, and biological pathways. In this context, the tools of Network Medicine are compatible as a platform to systematically explore not only the molecular complexity of a specific disease but may also lead to the identification of disease modules and pathways. Such an approach enables us to gain a better understanding of how environmental chemical exposures affect the function of human cells, providing better perceptions about the mechanisms involved and helping to monitor/prevent exposure and disease to chemicals such as benzene and malathion. We selected differentially expressed genes for exposure to benzene and malathion. The construction of interaction networks was carried out using GeneMANIA and STRING. Topological properties were calculated using MCODE, BiNGO, and CentiScaPe, and a Benzene network composed of 114 genes and 2415 interactions was obtained. After topological analysis, five networks were identified. In these subnets, the most interconnected nodes were identified as: IL-8, KLF6, KLF4, JUN, SERTAD1, and MT1H. In the Malathion network, composed of 67 proteins and 134 interactions, HRAS and STAT3 were the most interconnected nodes. Path analysis, combined with various types of high-throughput data, reflects biological processes more clearly and comprehensively than analyses involving the evaluation of individual genes. We emphasize the central roles played by several important hub genes obtained by exposure to benzene and malathion. Full article

The ontogenesis and yield formation in crop plants are modified by environmental conditions. Due to climatic change detected over two decades, the harmful influence of abiotic factors is increasing. One of the most threatening issues reducing plant productivity is drought stress. The strength of plant response to water shortages could differ depending on the strength of the drought stress, type of crop, genetic background, presence of additional stresses, and stage of plant development. There are examples of sexual hybridization between crop plants like oat (Avena sativa L.) and maize (Zea mays L.) with which stable fertile hybrids were generated. Additional maize chromosomes in oat plants (oat × maize addition, OMA) often infer morphological and physiological (e.g., PS II photosystem activity and chlorophyll production) changes modulated by the interaction of certain maize chromosomes added to the oat genome. The aim of the research was to evaluate the chosen physiological, biochemical, and agronomic parameters of OMA plants subjected to soil drought. Analysis of variance indicated that the main effects of genotype as well as treatment × genotype interaction were significant for all the traits studied (photosynthetic pigment content, selected PSII indices, mass of stem, number of grains/plant, mass of grains/plant). Most of the examined lines severely reduced PSII photosystem parameters, pigment content, and yield-related traits under drought stress. The results indicated that two lines (9 and 78b) retained high yielding potential under drought stress compared to commercial cv. Bingo. Full article