Search Results (1,147)

Rural road perception for agricultural machinery automation faces challenges including complex backgrounds, drastic lighting and weather variations, frequent occlusions, and high densities of small objects with significant scale variations. These factors make conventional detectors prone to missed detections and misclassifications. To address these issues, a 4K rural road dataset with 4771 images is constructed. The dataset covers 13 object categories and includes diverse day/night conditions and multiple weather scenarios on both structured and unstructured roads. EMAF-Net, a lightweight single-stage detector based on YOLOv4-P6, is proposed. The backbone integrates an EMHA module combining EfficientNet-B1 with multi-head self-attention (MHSA) for enhanced global context modeling while preserving efficient local feature extraction. The neck adopts an Improved ASPP and a bidirectional FPN to achieve robust multi-scale feature fusion and expanded receptive fields. Meanwhile, CIoU loss is used to optimize bounding box regression accuracy. The experimental results demonstrate that EMAF-Net achieves an mAP@0.5 of 64.05% and an mAP@0.5:0.95 of 48.95% on a rural road dataset. At the same time, it maintains a lightweight design with 18.3 M parameters and a computational complexity of 38.5 GFLOPs. Ablation studies confirm the EMHA module contributes a 6.22% mAP@0.5 improvement, validating EMAF-Net’s effectiveness for real-time rural road perception in autonomous agricultural systems. Full article

Melissopalynological analysis is essential for determining the botanical origin of honey, corbicular pollen and bee bread, as well as detecting adulteration. However, it traditionally relies on labor-intensive and subjective manual pollen identification. As a proof-of-concept preceding full honey analysis, this study evaluates artificial intelligence methods for automated pollen grain recognition under controlled conditions. Hazel (Corylus avellana L.) and dandelion (Taraxacum officinale F.H. Wigg.) were used as model taxa to validate the proposed approach before its application to real varietal honey samples. This study introduces a novel three-stage pipeline that decouples object detection from feature extraction, utilizing YOLOv12m for region-of-interest generation and, for the first time in melissopalynology, DINOv3 ConvNeXt-B for deep feature representation. Microscopic images acquired at 400× magnification yielded 2498 dandelion and 1941 hazel pollen grains. The detector achieved an mAP@0.5 of 0.936 with an F1 score of 0.88, while the classifier reached 98.1% accuracy with good class separability (Silhouette coefficient: 0.407). The primary technical contribution is the systematic optimization of the detection-to-classification interface. Context-aware bounding box expansion (12%) and an optimized IoU-NMS threshold (0.65) significantly improve the stability of morphological feature extraction, as confirmed by ablation studies. Computational cost reporting further supports reproducible, deployment-oriented comparison. The results confirm the feasibility of this AI-based framework as an intermediate step toward automated melissopalynological analysis, with future work focusing on standardized microscopy protocols and expanded pollen databases for varietal honey authentication. Full article

Radio-frequency (RF) impairments such as thermal noise, phase noise, and nonlinear distortion are inherently coupled in practical multiple-input multiple-output (MIMO) transceivers, yet most existing mitigation techniques treat them independently or rely on correlation-based black-box learning models. These approaches often fail to generalize under varying operating conditions because they do not capture the underlying causal relationships among hardware impairments. This paper proposes a causal representation learning framework that jointly models and mitigates coupled RF impairments by learning disentangled latent variables aligned with their physical causal structure. A causal variational autoencoder with a structured physics-informed prior and causal regularization is developed to recover impairment-specific representations and enable targeted compensation under diverse channel conditions. The framework is evaluated in a controlled MIMO simulation environment to systematically analyze impairment interactions and mitigation performance. Experimental results show that the proposed method significantly outperforms both classical receivers and conventional learning-based approaches. In particular, the framework achieves an average BER reduction of approximately 57% compared with the classical model-based receiver and about 30% relative to correlation-based deep learning models, while also outperforming recent variational autoencoder-based MIMO detectors in robustness under unseen operating conditions. The output signal-to-noise ratio improves by up to 2.2 dB across the evaluated SNR range. Furthermore, latent representation analysis shows a substantial reduction in cross-covariance, with the disentanglement score decreasing from above 0.48 in standard variational models to approximately 0.12 using the proposed causal approach. Under unseen combinations of SNR and impairment severity, the proposed model achieves the lowest BER degradation and a robustness score of 0.86, confirming improved generalization beyond the training distribution. These results demonstrate that causal representation learning provides a principled and effective solution for modeling and mitigating coupled RF impairments in MIMO communication systems. Full article

Soda lakes are ecologically significant habitats characterized by high salinity, alkaline pH, and intense evaporation. These milieus are hostile to most life, though these lakes could be a rich source for discovering novel halotolerant and halophilic cyanobacterial taxa. The Indian subcontinent is endowed with shallow saline–alkaline lakes whose cyanobacterial diversity has been little explored. The present study was undertaken to explore the cyanobacterial diversity in an inland saline–alkaline lake (Sambhar Lake) in India using a polyphasic approach. Two thin, filamentous strains encapsulated within thick sheaths and capable of nodule formation under normal light conditions were recovered. Both isolates exhibited growth at up to 4% salinity, indicating their halotolerant nature. The studied strains exhibited <95% 16S rRNA gene similarity with closely related taxa from the genera Thainema and Insularia and formed a distinct evolutionary lineage in phylogenetic tree supported by a high bootstrap value. Additionally, the secondary structures of the 16S-23S Internal Transcribed Spacer (ITS) regions (D1-D1′ and BoxB) of the studied strains showed remarkable differences from phylogenetically closely related taxa, indicating these strains represent a new genus in the Nodosilineales: Krienitziella sambharensis gen. et sp. nov., in accordance with the International Code of Nomenclature for Algae, Fungi, and Plants (ICN). Full article

This study proposes Optimized Skewness and Kurtosis Transformation (OSKT), a novel moment-targeting normality transformation that corrects asymmetry and peakedness in non-normal data. OSKT employs a transformation function derived from the Tukey g–h distribution, incorporating skewness and kurtosis parameters, and is optimized by minimizing a single objective function based on the Anderson–Darling test statistic. The optimization process uses L-BFGS-B to tune the transformation parameters to find the best fit for the standard normal distribution. OSKT ensures a balance between symmetry and tail behavior by minimizing deviations from theoretical normality. It has highly competitive performance compared to the alternative, Box–Cox, Yeo–Johnson transformations, including their robust variants and moment-matching Lambert W method, for normalizing complex distributions. According to our analysis, OSKT also achieves superior normalization for highly non-Gaussian data, successfully transforming highly resistant distributions, including approximately symmetric bimodal datasets, where other methods fail. Full article

The natriuretic peptides A and B, encoded by NPPA and NPPB, respectively, have complementary and redundant functions in cardiovascular homeostasis. To establish their coordinated roles, we analyzed the cardiac phenotype of a mouse line in which the Nppa–Nppb cluster was deleted from the genome. At 8 weeks of age, Nppa–Nppb^−/− mice (HOM) had significantly larger hearts and cardiomyocytic hypertrophy compared to wild-type and heterozygous mice. Electrocardiogram comparisons showed QRS prolongation in HOM mice. Hypertrophy was confirmed by echocardiography, which further indicated preservation of left ventricular systolic function. Bulk-transcriptomic analysis revealed moderate changes in gene expression of the left ventricle. Genes involved in fatty acid metabolism, ion handling and conductivity, including genes marking the ventricular conduction system, were down-regulated. Spatial transcriptomic analysis revealed the greatest changes in gene expression in the subendocardial wall, where the ventricular conduction system is located. Tbx5, the encoding dosage-sensitive T-box transcription factor Tbx5 that is essential for the expression of ventricular conduction system genes and for Nppa and Nppb, was down-regulated in the ventricles of HOM mice, indicating that a positive feedback loop normally maintains Tbx5 expression. We conclude that homozygous Nppa–Nppb deficiency in mice causes cardiac hypertrophy, including a likely perturbation of the ventricular conduction system. Full article

Silicon photonic integrated circuit (Si-PIC) components/devices based on silicon-on-insulator (SOI) waveguides have become critical components in modern optoelectronic information systems. This investigation systematically examines optical propagation losses (OPLs) induced by the sidewall roughness (SWR) of a waveguide through comparative analysis of two scattering-loss theoretical frameworks: the SWR-improved Payne–Lacey (P-L) three-dimensional (3-D) formalism and Hörmann’s 3-D perturbation model. Crucially, our computational results identify SWR = 10 nm as the convergence threshold where both models exhibit consistent OPL predictions across waveguide architectures. Single-mode SOI rib waveguides with 0.5 µm high ribs on 2.0 µm silicon film and a 2.0 μm BOX layer were designed and fabricated using the classic ICP-RIE technique. Furthermore, SWRs of 28 nm were obtained with confocal laser scanning microscopy for SOI waveguides, leading to OPLs of 2.66 and 2.67 dB/cm for TE and TM modes, respectively, from the 2-D SWR-enhanced P-L model, and 1.7 and 1.9 dB/cm, respectively, from the Hörmann 3-D model. Finally, the average experimental result of OPL for the same waveguide was 2.61 dB/cm, showing a strong agreement with the numerical values of the SWR-improved P-L 3-D formalism, providing a robust framework for optimizing industrial-grade SOI waveguide-based PIC devices/components. Full article

Colorectal cancer (CRC) is a significant cause of cancer mortality in the world, and its etiology is complicated by genetic and epigenetic changes. As one of the most important tumor progression regulators, Zinc Finger E-box Binding Homeobox 1 (ZEB1) is a transcription factor that has a key role in epithelial–mesenchymal transition (EMT), which is essential in the metastasis, drug resistance, and plasticity of cancer cells in CRC. ZEB1 silences the expression of epithelial markers, including E-cadherin, and it induces the development of mesenchymal properties, such as invasion and metastasis, i.e., tumor aggressiveness. ZEB1 drives epigenetic reprogramming in CRC by coordinating histone deacetylation, histone methylation, and DNA methylation of epithelial tumor suppressor gene promoters and by engaging in reciprocal regulatory interactions with non-coding RNAs, including the miR-200 family. Furthermore, multiple oncogenic signaling cascades, including Wnt/β-catenin, TGF-β, NF-κB, MEK-ERK, JAK/STAT3, and HIF-1α, converge on ZEB1 to amplify its transcriptional and epigenetic activity, positioning ZEB1 as a nodal integrator of extracellular cues and epigenetic reprogramming in CRC metastasis. This review integrates three interconnected regulatory layers, i.e., (1) ZEB1’s direct epigenetic control of target gene expression via histone modification and DNA methylation, (2) post-transcriptional regulation of ZEB1 itself by ncRNAs (miRNAs, circRNAs, and lncRNAs) that create feedback circuits modulating layer 1, and (3) upstream modulation of ZEB1 transcriptional activity by oncogenic signaling pathways (Wnt/β-catenin, TGF-β, NF-κB, MEK-ERK, JAK/STAT3, and HIF-1α) to provide a comprehensive picture of ZEB1 in CRC metastasis and its therapeutic implications. Full article

This study investigated how different LED spectral compositions affect seed germination, early growth, photosynthetic efficiency, and the postharvest quality and microbiological stability of Ipomoea aquatica Forsk. cultivated in a plant factory system, aiming to propose an integrated management strategy for stable year-round production. Five LED light treatments with varying red and blue light ratios (R10, R7B3, R5B5, R3B7, and B10) were applied during cultivation. After harvest, the plants were stored under low-temperature conditions using either carton box packaging or modified atmosphere packaging (MAP) to evaluate postharvest quality and microbial changes. Germination analysis indicated that red-dominant treatments (R10 and R7B3) significantly enhanced germination percentage, rate, and uniformity. These treatments also promoted greater plant height and fresh biomass accumulation during early growth while maintaining a higher maximum quantum yield of photosystem II (Fv/Fm), indicating improved photochemical efficiency. In contrast, blue-dominant treatments led to reduced growth performance and lower Fv/Fm values. Postharvest quality and microbiological stability were more significantly affected by the packaging method than by the LED light treatment. MAP effectively minimized fresh weight loss and inhibited the growth of aerobic bacteria, Escherichia coli, total coliforms, and yeast and mold during storage. Overall, the findings demonstrate that red-centered LED spectra are optimal for enhancing early growth and physiological stability of I. aquatica, while MAP is crucial for preserving postharvest quality and microbial safety. This study underscores the synergistic potential of combining LED spectral management during cultivation with optimized packaging strategies to achieve stable year-round production and extended shelf life of I. aquatica in controlled plant factory systems. Full article

One major challenge faced by deep learning-based methods that detect target objects in the form of bounding boxes is object occlusion. High degrees of occlusion significantly diminish the accuracy of instance segmentation. Nonetheless, complex-valued Fourier descriptors can robustly represent object boundaries using minimal information. In this study, the impact of integrating Fourier descriptors—renowned for their strong representational capacity—with deep network models (UNet) that exhibit high generalization performance on instance segmentation accuracy was investigated. Within the scope of the research, nine network models were designed based on different strategies for utilizing frequency components. These variants fall into four strategy families: (i) UNet-style spectrum regression on fixed low-frequency windows (FUNet), (ii) magnitude-guided frequency selection/ROI construction (FUNet–Thr, FUNet–BBox), (iii) sequence models over tokenized FFT coefficients (BiLSTM Patch/Sorted), and (iv) encoder-only spectrum predictors with different depth/capacity (EncoderFFT1/2). To fairly evaluate the models’ performance in segmenting objects subjected to disruptive factors (e.g., occlusion, blurring, noise), a specialized synthetic dataset was prepared. The task is formulated as single-target (single-instance), single-class segmentation. This dataset, automatically generated according to initial parameter values, contains images of objects moving at various speeds within a single frame. Among these models, the one termed FUNet, which relies on partial matching of central frequency components, achieved the highest segmentation accuracy despite the disruptive effects. Under the challenging Dataset 8 setting, the proposed FUNet achieved the highest overlap-based performance (Dice = 0.9329, IoU = 0.8842) among Attention U-Net, U-Net, and FourierNet, with statistically significant gains confirmed by paired per-image tests. Full article

Aging is associated with complex physiological changes that influence drug pharmacokinetics, including alterations in mitochondrial function and gastrointestinal (GI) drug transporter activity. Mitochondrial dysfunction—characterized by reduced oxidative phosphorylation, mitochondrial DNA damage, and increased reactive oxygen species—is a hallmark of aging and may affect energy- and redox-dependent cellular processes in the gut. At the same time, aging can modulate the expression and function of key intestinal drug transporters from the ATP-binding cassette (ABC) and solute carrier (SLC) families, which play a central role in oral drug absorption and bioavailability. This review examines the molecular links between age-related mitochondrial dysfunction and regulation of GI drug transporters, with a focus on their pharmacokinetic consequences in older adults. We summarize evidence of mitochondrial decline in the aging intestine and discuss how mitochondrial signals—such as cellular energy status and oxidative stress—regulate transporter expression and activity via pathways including AMPK (AMP-Activated Protein Kinase), Sirtuin–FOXO (Forkhead box O transcription factors), Nrf2 (Nuclear factor erythroid 2-related factor 2), and NF-κB (Nuclear Factor kappa B). We highlight clinical examples of drugs showing age-related changes in bioavailability that may be attributable to transporter dysfunction. Finally, we discuss therapeutic implications for geriatric pharmacotherapy, including dose adjustment, management of transporter-mediated drug–drug interactions, and strategies aimed at preserving mitochondrial health. Full article

DHX8 encodes a DEAH-box RNA helicase, an ATP-dependent enzyme that plays essential roles in RNA metabolism, including pre-mRNA splicing, transcription, and mRNA decay. Although DHX8 dysfunction has been linked with developmental abnormalities and disease pathogenesis in multiple model organisms, its biological functions in Lepidoptera, particularly in the silkworm Bombyx mori, remain unknown. To investigate the developmental role of B. mori DHX8 (BmDHX8), we generated knockout mutants using CRISPR-Cas9 genome editing. Genome sequencing confirmed frameshift mutations in the BmDHX8 locus. BmDHX8 mutants exhibited severe developmental defects such as dramatically reduced body size and premature lethality of silkworm larvae. Molecular characterization suggested systemic dysregulation, as evidenced by decreased triglyceride accumulation, impaired mTOR signaling activity, and increased aberrant splicing events. Therefore, these results indicate that loss of BmDHX8 is associated with aberrant splicing and alterations in lipid homeostasis and mTOR signaling pathways, potentially contributing to developmental defects. Taken together, our study offers an initial functional knockout analysis of BmDHX8 in regulating larval development in silkworms. Full article

Background: One of the main causes of cancer-related mortality globally is lung adenocarcinoma (LUAD), necessitating the development of novel therapeutic targets. The parathyroid hormone type 2 receptor (PTH2R) exhibits differential expression across multiple cancers, yet its role in LUAD remains unclear. Methods: Through an integrated analysis of multiple public databases (including SangerBox 3.0, UALCAN, Kaplan–Meier Plotter, and TIMER), we identified PTH2R—a member of the family B1 GPCRs—as a candidate therapeutic target with significant prognostic value in LUAD. Subsequently, the antitumor effects of PTH2R knockdown and melatonin were evaluated through cell proliferation, colony formation, migration, and apoptosis assays. Transcriptome analysis revealed key biological processes and signaling pathways regulated by PTH2R, identified key genes modulated by PTH2R, and validated core gene expression via RT-qPCR. Results: PTH2R is a potential therapeutic target for lung adenocarcinoma. Both PTH2R knockdown and melatonin treatment significantly inhibited LUAD cell proliferation, colony formation, and migration capabilities while promoting apoptosis. Notably, the combination of PTH2R knockdown and melatonin treatment demonstrated synergistically enhanced antitumor effects. Transcriptome analysis revealed two key genes within the PTH2R signaling pathway, and RT-qPCR validated the expression of these two key genes. Conclusions: Our work provides the first evidence confirming the substantial value of PTH2R as a novel therapeutic target for LUAD. It preliminarily demonstrates the mechanism by which melatonin inhibits LUAD by targeting PTH2R, offering crucial experimental evidence and theoretical support for developing precision therapeutic strategies against this cancer. Full article

Paired box (PAX) genes encode a family of nine transcription factors that function as master regulators of embryogenesis, organogenesis, and lineage specification. Their tightly regulated spatial and temporal expression is essential for the development of multiple organ systems, including the central nervous system, eyes, kidneys, immune system, musculoskeletal system, and endocrine organs. Germline mutations of PAX genes result in a broad and often pleiotropic spectrum of human disease, reflecting the developmental programs governed by each family member. Pathogenic variants in PAX genes underlie diverse congenital disorders such as aniridia (PAX6), renal coloboma syndrome (PAX2), otofaciocervical syndrome with immunodeficiency (PAX1), Waardenburg syndrome (PAX3), maturity-onset diabetes of the young (PAX4), and tooth agenesis (PAX9). These conditions frequently demonstrate variable expressivity, incomplete penetrance, and overlapping phenotypes, which make it challenging to be clinically recognized. Beyond embryogenesis and embryologic development, emerging evidence indicates that several PAX proteins remain active in postnatal tissue maintenance, adult stem cell regulation, immune function, and regenerative responses (particularly PAX7 in skeletal muscle satellite cells and PAX5 in B-cell homeostasis), further expanding their clinical relevance. This review provides a synopsis of the major, clinically relevant, germline PAX gene mutations, emphasizing genotype–phenotype correlations, developmental mechanisms, and disease classification across the organ systems. By integrating molecular genetics with human pathology, we highlight the diagnostic implications of PAX genes as central determinants of congenital disease and provide a framework for understanding how alterations in the developmental transcriptional networks translate into human pathology. Full article

To address the challenges of thin cloud detection and imprecise cloud boundary segmentation in Landsat 8 remote sensing imagery, this paper proposes a systematic approach that comprehensively enhances cloud detection accuracy from data preprocessing to network architecture optimisation. First, through empirical analysis, an optimised band input combination was determined (removing the panchromatic Band 8 and thermal infrared Band 11), effectively suppressing urban background noise. Subsequently, an enhanced ResUNet model was designed, innovatively integrating an Atrous Spatial Pyramid Pooling (ASPP) module with an attention gate (AG) mechanism. The ASPP module enhances detection capabilities for thin clouds and diffuse cloud masses by aggregating multi-scale global contextual information. The attention-gated mechanism finely tunes feature fusion during the decoding phase, suppressing interference from highly reflective surface features to achieve precise cloud boundary segmentation. Experiments conducted on the Landsat 8 dataset featuring typical urban scenes demonstrate that the proposed method significantly outperforms mainstream models across both conventional and boundary-specific metrics, achieving an overall accuracy (OA) of 0.9717, a mean intersection over union (mIoU) of 0.8102, and, notably, a mean bounding box intersection over union (mB-IoU) of 0.4154 and a mean bounding box F1 score of 0.5356, representing improvements of 16.3% and 12.5%, respectively, over existing methods. This research provides an efficient and robust technical framework for cloud detection tasks in complex urban environments, laying the foundation for high-precision processing of remote sensing imagery and subsequent quantitative analysis. Full article