Search Results (201)

Accurate leukocyte segmentation remains challenging in automated hematological analysis due to staining variability, heterogeneous imaging conditions, and morphological diversity across cytological datasets, severely limiting deep learning model generalization. This work proposes a dual-module framework designed to achieve stain-invariant and robust leukocyte segmentation. The first module performs explicit stain standardization by combining a VGG-based encoder, a transformer bottleneck, and a convolutional decoder to harmonize diverse inputs toward a Wright–Giemsa reference appearance. The second module introduces a multi-encoder segmentation architecture integrating complementary spatial, leukocyte-specific, and nucleus-focused representations extracted from multiple color spaces. The framework is evaluated on six public and clinical datasets covering multiple staining protocols, magnifications, and imaging scenarios. Experimental results demonstrate consistent high performance, with Dice coefficients exceeding 96% on most datasets and systematic improvements over state-of-the-art methods. Extensive ablation studies confirm the synergistic contributions of stain-standardization and multi-encoder fusion to model robustness and cross-dataset generalization. This framework overcomes stain variability and domain shift, offering a practical tool for automated leukocyte analysis in clinical settings. Full article

Hyperspectral imaging (HSI) extends conventional histopathology by combining spatial morphology with rich spectral information that reflects tissue biochemical composition, offering new opportunities for quantitative tissue analysis. However, reliable spectral analysis requires accurate instance-level segmentation of cell nuclei to enable the construction of meaningful nuclear spectral databases. In this work, a comprehensive methodology for generating hyperspectral databases of cell nuclei from histopathological samples is presented, including hyperspectral acquisition, preprocessing, nucleus segmentation, and spectral signature extraction. Three nucleus segmentation methods are evaluated: a spectral-only approach based on pixel-wise hyperspectral signatures in the visible–VNIR range; a spatial-only approach using synthetic RGB images derived from hyperspectral cubes; and a combined spatial–spectral approach that jointly exploits spatial and spectral information. The methods are assessed on a proprietary dataset of 30 hyperspectral cubes of tumor and healthy histopathological brain tissue annotated by expert pathologists. The spectral-only method achieves a Dice similarity coefficient (DSC) of 61.89% and produces severe over-segmentation, with cell count deviations exceeding substantially the ground truth in healthy tissue. The spatial-only method attains the highest pixel-wise accuracy (78.97% DSC) but underestimates nucleus counts by approximately 30% in tumor regions due to nucleus merging. The spatial–spectral method achieves a DSC of 73.13% and a mean cell count deviation of 4%, providing more reliable instance-level separation. These findings demonstrate that pixel-wise accuracy alone is insufficient for hyperspectral nuclear database generation. Full article

Major orthopedic limb surgery is often accompanied by external coaptation; the combined effect of these interventions can lead to muscle atrophy and functional impairment. Large animal models, including goats, are commonly used to study orthopedic interventions, yet longitudinal data on muscle changes after such interventions are limited. This study quantified gastrocnemius muscle adaptations in adult Boer-cross goats undergoing a clinically representative unilateral tibial segmental ostectomy and external coaptation protocol. Muscles on the operated side exhibited statistically significant decreases in mass, length, optimal fiber length, and CSA, and increases in nucleus density compared to muscles on the contralateral, non-operated side (p < 0.05). Although muscle properties showed partial recovery over time, mass and CSA remained 20–30% lower on the operated side than on the non-operated side at 12 months post-surgery despite cast removal at about 2 months post-surgery. Muscle CSA was positively correlated with bone mineral density and peak vertical ground reaction forces measured during the in vivo study. The extent of muscle recovery in the goat model was less than that observed for other mammalian models of hindlimb remobilization. More research is needed to understand the complex interaction between surgery, external coaptation, and muscle properties in the goat model. Full article

Background/Objectives: Variations in staining styles—arising from differences in tissue preparation, scanners, and laboratory protocols—severely compromise the robustness of automated cell segmentation algorithms in digital pathology. Moreover, manual nucleus annotation is extremely labor-intensive, leading to a scarcity of large-scale, fully annotated datasets for supervised nucleus segmentation. This study proposes a novel framework that simultaneously mitigates staining variability and achieves high-accuracy nucleus segmentation using only minimal annotations. Methods: We present 3SGAN, a multi-task dual-branch generative adversarial network (GAN) that jointly performs stain normalization and nucleus segmentation in a semi-supervised manner. The framework adopts a teacher–student paradigm: a lightweight teacher model (AttCycle) equipped with attention gates generates reliable pseudo-labels, while a high-capacity student model (TransCycle) leveraging a hybrid CNN–Transformer architecture further refines performance. 3SGAN was trained and evaluated on a large dataset of 1408 Whole-Slide Images (WSIs) from two medical institutions, encompassing 101 distinct staining styles, with nucleus-level annotations required for only 5% of the data. Results: 3SGAN significantly outperformed state-of-the-art methods, achieving superior segmentation accuracy with an F1-score of 0.8140, mean IoU of 0.8201, and AJI of 0.6915. Simultaneously, it demonstrated substantial improvements in stain normalization quality, yielding a low RMSE of 0.0908, high PSNR of 21.0615, and SSIM of 0.8556 on the internal test set. External validation on independent MoNuSeg and PanNuke datasets, as well as on previously untested tumor-rich non-ROI regions from our in-house WSIs, confirmed strong generalizability with excellent stain normalization and top-tier segmentation accuracy across diverse staining protocols, tissue types, and pathological patterns. Conclusions: The proposed 3SGAN framework demonstrates that high-performance nucleus segmentation and stain normalization can be achieved with minimal annotation requirements, offering a practical and scalable solution for digital pathology applications across diverse clinical settings and staining protocols. Full article

Growth Regulating Factors (GRFs) are plant-specific transcription factors that play crucial roles in regulating growth and development throughout the plant life cycle. A total of 34 Gossypium hirsutum GRF family genes were identified at the genome-wide level, which were unevenly distributed on 19 chromosomes, and were predicted to be mainly localized in the nucleus and plasma membrane. The number of GRF family genes varied greatly among different species, and they were categorized into four subfamilies (I–IV) according to their phylogenetic relationships. The G. hirsutum GRF genes possessed specific highly conserved structural domains, Trp-Arg-Cys motif (WRC) and Gln, Leu, Gln motif (QLQ), and structural analysis of the genes revealed that they contained 1–23 exons, and most of them contained UTRs. Intraspecies covariance analysis revealed that the GRF genes expanded in G. hirsutum by segmental duplication. The promoter region of the G. hirsutum GRF gene contained a large number of adversity stress response elements, as well as a small number of hormone response elements and growth and development-related response elements. Transcriptome data showed that the expression of G. hirsutum GRF genes was significantly higher in leaves than in other tissues, and some GRF genes responded to a variety of abiotic stresses. Additionally, transcriptomic sequencing revealed significantly higher expression levels of GhGRFs (e.g., GhGRF13/14/18) in embryonic callus (EC) compared to non-embryonic callus (NEC). This differential expression was validated by RT-qPCR, which confirmed that GhGRF13/14/16/20 were significantly upregulated in EC relative to NEC. These findings provide valuable candidate genes and molecular insights for improving G. hirsutum regeneration efficiency and yield-related traits through genetic manipulation, thereby accelerating the molecular breeding of elite G. hirsutum varieties. Full article

Background: Deep brain stimulation (DBS) has traditionally followed diagnosis-driven, nucleus-centered targeting paradigms. Increasing evidence supports a circuit-based framework in which clinical outcomes depend on modulation of symptom-relevant networks rather than diagnostic labels alone. This approach is particularly relevant in mixed movement disorder phenotypes such as dystonic tremor, where the most disabling symptom may not align with the conventional surgical target. Methods: We report a clinically illustrative single case treated using a symptom-oriented, connectome-informed DBS strategy. Clinical phenotype, tremor severity, functional impairment, prior medical and botulinum toxin treatments, and longitudinal outcomes were systematically reviewed. DBS target selection prioritized the dominant, treatment-refractory symptom rather than the underlying dystonia diagnosis. Surgical planning incorporated high-resolution MRI with patient-specific thalamic segmentation using Brainlab Brain Elements^®, followed by postoperative lead localization and volume of tissue activated visualization with the SureTune™ platform. Results: A 54-year-old left-handed woman with long-standing cervical dystonia developed a severe, markedly asymmetric dystonic tremor predominantly affecting the left upper limb, resulting in profound functional disability. Instead of conventional bilateral globus pallidus internus DBS, unilateral right ventral intermediate nucleus (VIM) DBS was selected to engage tremor-related cerebellothalamic circuits. Rapid and marked improvement was observed, with tremor severity reduced to mild levels within 15 days after stimulation onset. At 6-month follow-up, overall tremor severity improved from 49 to 13 points on the Fahn–Tolosa–Marin Tremor Rating Scale, corresponding to a 73.5% reduction. This improvement was associated with restoration of legible handwriting, independent feeding and drinking, and recovery of bimanual fine motor function. Clinical benefit remained stable throughout follow-up, without stimulation-related adverse effects. Conclusions: This case illustrates the feasibility of a symptom-oriented, connectome-informed DBS strategy in selected patients with dystonic tremor. When symptom expression and network involvement are markedly asymmetric, selective unilateral modulation of the tremor-dominant circuit may achieve meaningful and durable functional improvement. Further studies are needed to assess the generalizability of this approach. Full article

The genus Blunervirus comprises plant viruses that infect a diverse range of plants, but no blunervirus has been reported infecting elm trees (Ulmus parvifolia) in China to date. Using high-throughput sequencing and reverse-transcription PCR assays, a novel blunervirus, tentatively named elm blunervirus 1 (ElmBlV1), was identified from a symptomatic elm plant (Ulmus parvifolia) in China. The genome of ElmBlV1 harbors canonical molecular features of blunerviruses and comprises six RNA segments (RNAs1–6), with RNA5 and 6 being two additional genomic components not reported in known blunerviruses. Sequence analyses revealed amino acid (aa) identity of ElmBlV1 proteins ranging from 25.9% (polyprotein encoded by RNA1) to 64.2% (movement protein encoded by RNA4) relative to reported blunerviruses and include five orphan open reading frames. Phylogenetically, ElmBlV1 is most closely related to blueberry necrotic ring blotch virus. Furthermore, ElmBlV1 P37 localizes to both plasmodesmata and the nucleus. Additionally, the RNA reads mapping revealed high read coverage was observed on RNAs3–4 for this virus. To our knowledge, this is the first report of a blunervirus infecting an elm tree in China. Our results enrich the diversity of known viruses in the genus of Blunervirus and expand our understanding of their genomic characteristics and molecular biology. Full article

Soil salinization severely restricts the sustainable development of the pear industry. Pyrus betulifolia, a vital native salt-tolerant rootstock in China, holds great significance for investigating stress resistance mechanisms. Plant-specific DNA-binding One Zinc Finger (Dof) transcription factors act as pivotal regulators in stress adaptation. However, their functions in P. betulifolia remain largely unexplored. In this study, we identified 43 PbeDof members within the P. betulifolia genome and classified them into eight subfamilies via phylogenetic analysis. Gene structure and conserved motif analyses revealed that PbeDof members within the same subfamily share similar exon-intron organizations and protein architecture, suggesting evolutionary conservation. Promoter analysis indicated that PbeDof genes are rich in cis-acting elements related to light, phytohormones (especially ABA and MeJA), and stress responses, implying their potential roles in diverse biological processes. Chromosomal localization and collinearity analyses revealed that segmental duplication was the primary driver of this family’s expansion. Combined transcriptomic profiling and qRT-PCR assays demonstrated that PbeDof9.1 is predominantly expressed in roots and is strongly induced by salt stress. Subcellular localization confirmed that PbeDof9.1 targets the nucleus. Functional characterization indicated that heterologous overexpression of PbeDof9.1 in Arabidopsis thaliana significantly enhances salt tolerance at germination and seedling stages. Notably, under 175 mM NaCl stress, the transgenic lines exhibited a superior root system architecture, with primary root length and lateral root numbers being approximately 1.5-fold higher than those of the wild type. Furthermore, homologous overexpression in pear calli confirmed that PbeDof9.1 mitigates oxidative damage by boosting the activities of peroxidase (POD) and catalase (CAT) to scavenge reactive oxygen species (ROS), thereby reducing malondialdehyde (MDA) accumulation. Collectively, this study characterizes the PbeDof family and establishes PbeDof9.1 as a key candidate gene for the genetic improvement of salt tolerance in pear rootstocks. Full article

This study presents a finite element (FE) investigation of intervertebral disc (IVD) degeneration in the human lumbar spine (L1–L3 segment). The model, based on CT-derived geometry and isotropic hyperelastic representation of disc tissues, incorporates controlled simplifications, detailed in the limitations section. Degenerative changes were parametrically simulated across healthy, mild, moderate, and severe stages by reducing disc height (up to 60%), nucleus pulposus volume (up to 70%), and adjusting tissue stiffness to reflect dehydration and fibrosis. Displacement-controlled compressive loading was applied to assess von Mises stress distributions, reaction forces, and load transfer mechanisms. Results indicate significant load redistribution: annulus fibrosus stresses increased by up to 175% in severe degeneration, while nucleus pulposus stresses decreased by ~70%, indicating a diminished compressive load-bearing contribution of the nucleus. Model predictions were validated against cadaveric and in vivo data, confirming trends in intradiscal pressure (IDP) reductions (40–70%) and stress elevations. The parametric framework elucidates interactions between geometric and material changes, providing clinicians with insights into degeneration progression and guiding biomedical engineers in implant design and interventions. Full article

WRKY transcription factors serve as key regulators in plants, playing important roles in growth and development, secondary metabolism, and stress responses. Here, a comprehensive genome-wide analysis identified 58 WRKY genes (LcWRKYs) in litchi for the first time. All LcWRKY proteins were predicted to be hydrophilic and localized in the nucleus. Phylogenetic analysis classified them into three major groups (Groups I, II, and III), with a pronounced expansion of Group II, which contained 42 members divided into five subgroups. Members within the same phylogenetic clade exhibited highly similar exon–intron structures and conserved motif compositions, indicating strong evolutionary conservation. LcWRKYs were unevenly distributed across the litchi chromosomes, with chromosome 3 showing the highest gene density. Collinearity analysis suggested that both segmental and tandem duplications contributed to the evolutionary expansion of this family. Notably, promoter cis-acting element analysis revealed that LcWRKYs are enriched with light-responsive, hormone-responsive (e.g., ABA, MeJA, SA), and stress-responsive elements, suggesting their potential involvement in integrating light signaling, hormonal pathways, and environmental stress responses. Integrative expression analysis further revealed that multiple LcWRKYs were significantly up-regulated during the middle and late stages of fruit development in cultivars such as ‘Feizixiao’ and ‘Nuomici’. Consistent with these patterns, qRT-PCR validation demonstrated a pronounced induction of four representative genes (LITCHI004628.m1, LITCHI018082.m1, LITCHI021964.m1, and LITCHI030932.m1) at 40 days post-anthesis, followed by gene-specific expression trajectories at later stages, indicating their potential involvement in regulating fruit development, particularly during the mid-developmental stage. Altogether, the results of this study provide insight into the expansion and potential functional diversification of WRKY transcription factors in litchi and identify candidate regulators associated with fruit development, offering valuable targets for future functional studies and genetic improvement. Full article

Data transmission requirements are a major constraint for mission design and can increase mission complexity significantly. Thus, reducing the amount of data required to be transmitted is key. In this work, the reference scenario of the European Space Agency’s Comet Interceptor mission, specifically its Optical Periscopic Imager for Comets (OPIC) instrument, is used to assess the possibilities for onboard data selection through convolutional neural networks. In this study, we train various semantic segmentation and object detection networks to automatically determine the most scientifically interesting area on a fly-by image of a comet, focusing on the nucleus and inner coma, and investigate the impact this could have on data reduction. In the context of computational complexity, the average dice coefficient dropped by 0.07 between the best performing and the smallest network for semantic segmentation and by 0.11 for detection networks. While this drop is significant, the more computationally complex networks did not lead to any significant accuracy improvement. Based on the results, we can conclude that using convolutional neural networks is a feasible strategy for reducing data budgets in comet fly-by missions and that even the simplest segmentation networks tested can achieve a meaningful performance, showing that this approach is even feasible on hardware that can be compatible for launch or has already been used in space. Full article

Accurate segmentation of nuclei and cytoplasm in cervical cytology images plays a pivotal role in characterizing cellular morphology. The primary challenge is to precisely delineate boundaries within densely clustered cells, which is complicated by low-contrast edges and irregular morphologies. This paper introduces a novel framework combining MaskDino architecture with Hausdorff distance loss, enhanced by a two-phase training strategy. The method begins by employing MaskDino for precise nucleus segmentation. Building on this foundation, the framework then enhances cytoplasmic boundary detection in cellular clusters by incorporating a Hausdorff distance loss, with weight transfer initialization ensuring feature consistency across tasks.. The symmetry between the nucleus and cytoplasm servers as a key morphological indicator for cell assessment, and our method provides a reliable basis for such analysis. Extensive experiments demonstrate that our method achieves state-of-the-art cytoplasm segmentation results on the ISBI2014 dataset, with absolute improvements of 2.9% in DSC, 1.6% in TPRp and 2.0% in FNRo. The performance of nucleus segmentation is better than the average level. These results validate the proposed framework’s effectiveness for improving cervical cancer screening through robust cellular segmentation. Full article

Cell nucleus segmentation in microscopy images is an initial step in the quantitative analysis of imaging data, which is crucial for diverse biological and biomedical applications. While traditional machine learning methodologies have demonstrated limitations, recent advances in U-Net models have yielded promising improvements. However, it is noteworthy that these models perform well on balanced datasets, where the ratio of background to foreground pixels is equal. Within the realm of microscopy image segmentation, state-of-the-art models often encounter challenges in accurately predicting small foreground entities such as nuclei. Moreover, the majority of these models exhibit large parameter sizes, predisposing them to overfitting issues. To overcome these challenges, this study introduces a novel architecture, called mA-UNet, designed to excel in predicting small foreground elements. Additionally, a data preprocessing strategy inspired by road segmentation approaches is employed to address dataset imbalance issues. The experimental results show that the MIoU score attained by the mA-UNet model stands at $95.50\%$, surpassing the nearest competitor, UNet++, on the 2018 Data Science Bowl dataset. Ultimately, our proposed methodology surpasses all other state-of-the-art models in terms of both quantitative and qualitative evaluations. The mA-UNet model is also implemented in VHDL on the Zynq UltraScale+ FPGA, demonstrating its ability to perform complex computations with minimal hardware resources, as well as its efficiency and scalability on advanced FPGA platforms. Full article

Objectives: The Eight-chop technique is a mechanically based nuclear segmentation method designed to improve surgical efficiency and reduce intraocular tissue stress during phacoemulsification. Early postoperative aqueous flare serves as an objective indicator of surgical invasiveness, whereas corneal endothelial cell density (CECD) loss represents a structural measure of endothelial injury. Although both parameters are clinically important, their relationship has not been systematically investigated in the context of this newer mechanical fragmentation approach. Methods: This prospective observational study included 118 eyes from 70 non-diabetic patients undergoing uncomplicated Eight-chop phacoemulsification. Aqueous flare was measured preoperatively and at postoperative Day 1, Day 7, Week 7, and Week 19 using laser flare photometry. CECD was evaluated preoperatively and at Weeks 7 and 19. Changes over time were analyzed using paired t-tests. Linear mixed-effects models (random intercept = patient ID) were constructed to assess predictors of CECD loss and postoperative intraocular pressure (IOP) reduction. Explanatory variables included Day 1 flare, age, preoperative CECD, nucleus hardness (Emery-Little grade), cumulative dissipated energy (CDE), and irrigation fluid volume. Results: Postoperative flare increased significantly at all time points (all p < 0.001), peaking on Day 7 (16.7 ± 9.21 photon counts/ms). CECD loss was extremely small, averaging 1.38% at Week 7 and 1.46% at Week 19. In mixed-effects models, Day 1 flare was not associated with CECD loss at Week 7 (p = 0.35) or Week 19 (p = 0.85). Significant predictors of CECD loss included Emery-Little grade (p = 0.004 at Week 7; p = 0.025 at Week 19), with borderline contributions from CDE and irrigation volume. IOP decreased significantly at Weeks 7 and 19; however, Day 1 flare did not predict IOP reduction. Conclusions: Eight-chop phacoemulsification produced uniformly low postoperative inflammation and exceptionally small corneal endothelial cell loss. Early postoperative flare did not predict CECD loss, suggesting that the Eight-chop technique provides a highly standardized, low-invasiveness surgical environment. These findings suggest that the Eight-chop technique lowers ultrasound energy requirements and may help reduce corneal endothelial stress relative to standard phacoemulsification. Full article

Growth-regulating factors (GRFs) are plant-specific transcription factors that play important roles in plant growth and development. However, no systematic analysis of GRF genes has been reported in oat (Avena sativa). In this study, we conducted a comprehensive characterization of the GRF gene family in oat, including their physicochemical properties, chromosomal distribution, phylogenetic relationships, gene structure, conserved domains, promoter cis-elements, duplication events, and drought-responsive expression. In total, 28 GRF genes were identified in oat. Phylogenetic analysis classified them into two main groups, which could be further subdivided into five subgroups. Gene structure and conserved motif analyses revealed that AsGRF genes are largely group-specific and relatively highly conserved within each subgroup. Segmental duplication has been the primary driver of AsGRF gene family expansion, and these genes have undergone strong purifying selection during evolution. Transcriptomic analysis identified 13 AsGRF genes expressed under drought stress. Subsequent qRT-PCR analysis revealed that six of these genes were significantly up-regulated. Notably, AsGRF3 showed the highest expression level, was localized to the nucleus, and lacked transcriptional self-activation activity. In conclusion, this study provides a comprehensive analysis of the AsGRF gene family and serves as a valuable reference for further functional characterization of these genes in drought stress responses in oat. Full article