Search Results (19,903)

The C9ORF72 gene mutation is a major cause of amyotrophic lateral sclerosis (ALS). Disease mechanisms involve both loss of C9ORF72 protein function and toxic effects from hexanucleotide repeat expansions. Although its role in neurons and the immune system is well studied, the impact of C9ORF72 deficiency on skeletal muscle is not yet well understood, despite muscle involvement being a key feature in ALS pathology linked to this mutation. This study examined skeletal muscle from C9ORF72 knockout mice and found a 19.5% reduction in large muscle fibers and altered fiber composition. Ultrastructural analysis revealed mitochondrial abnormalities, including smaller size, pale matrix, and disorganized cristae. Molecular assessments showed increased ex-pression of Atrogin-1, indicating elevated proteasomal degradation, and markers of enhanced autophagy, such as elevated LC3BII/LC3BI ratio, Beclin-1, and reduced p62. Mitochondrial quality control was impaired, with a 3.6-fold increase in PINK1, upreg-ulation of TOM20, reduced Parkin, and decreased PGC-1α, suggesting disrupted mi-tophagy and mitochondrial biogenesis. These changes led to the accumulation of damaged mitochondria. Overall, the study demonstrates that C9ORF72 is critical for maintaining muscle protein and mitochondrial homeostasis. While C9orf72-haploinsufficiency does not directly compromise muscle strength in mice, it may increase the vulnerability of skeletal muscle in C9ORF72-associated ALS. Full article

RNA methylation, particularly N6-methyladenosine (m⁶A) and 5-methylcytosine (m⁵C), functions as a pivotal post-transcriptional regulatory mechanism and plays a central role in plant growth, development, and stress responses. This review provides a systematic summary of recent advances in RNA methylation research in cucurbit crops. To date, high-throughput technologies such as MeRIP-seq and nanopore direct RNA sequencing have enabled the preliminary construction of RNA methylation landscapes in cucurbit species, revealing their potential regulatory roles in key agronomic traits, including fruit development, responses to biotic and abiotic stresses, and disease resistance. Nevertheless, this field remains in its early stages for cucurbit crops and faces several major challenges: First, mechanistic understanding is still limited, with insufficient knowledge regarding the composition and biological functions of the core protein families involved in methylation dynamics—namely, “writers,” “erasers,” and “readers.” Second, functional validation remains inadequate, as direct evidence linking specific RNA methylation events to downstream gene regulation and phenotypic outcomes is largely lacking. Third, resources are scarce; compared to model species such as Arabidopsis thaliana and rice, cucurbit crops possess limited species-specific genetic data and genetic engineering tools (e.g., CRISPR/Cas9-based gene editing systems), which significantly hampers comprehensive functional studies. To overcome these limitations, future research should prioritize the development and application of more sensitive detection methods, integrate multi-omics datasets—including transcriptomic and methylomic profiles—to reconstruct regulatory networks, and conduct rigorous functional assays to establish causal relationships between RNA methylation modifications and phenotypic variation. The ultimate objective is to fully elucidate the biological significance of RNA methylation in cucurbit plants and harness its potential for crop improvement through genetic and biotechnological approaches. Full article

Smart Electromagnetic Skins (SESs) provide a cost-effective and efficient alternative to in- creasing the number of Base Stations (BSs) for improving the performance of next-generation communication networks and contribute to the implementation of Smart Radio Environ- ments (SREs). SESs generalize the concept of ReflectArrays (RAs) because they redirect the incident field in a non-specular direction. However, as the difference between the pointing and specular directions increases, specular and parasitic effects arise, which affect the radiation pattern, energy efficiency, and pointing direction. The techniques generally adopted for SES design, using homogenized-effective-medium model, are unable to overcome this drawback efficiently. Starting with initial SES design based on the Phase-Gradient (PG) approach, the suppression of the higher order modes has been achieved by incorporating volumetric charge-current distributions when defining radiation modes, using theory of electromagnetic-multipoles. This approach reveals formation of anapoles in single-layer SESs/RAs for first time ever. By combining both local and non-local approaches in super-cell design, higher-order symmetry-breaking of unit cells is utilized to exploit anapole formation as a parasitic mode suppression method. Numerical analysis of SESs with increasing size confirms the effectiveness of the proposed approach, which allows for a drastic reduction in parasitic modes while leaving the performance of the desired mode unchanged. Adopting a multipole perspective enhances the understanding of SES radiation mechanisms, unlocks their unexploited performance potential, and opens new opportunities for multifunctional design. Full article

Background/Objective: This study aims to better characterize the clinical presentation, histology, and imaging features of breast sarcomas on mammography, ultrasound, and MRI, in addition to analyzing the effectiveness of AI DS in detecting breast sarcomas. Methods: A retrospective review from 2008–2024 yielded 18 patients with histologically proven breast sarcomas with imaging available for review. Mammography was available for 13 lesions, ultrasound for 19 lesions, and MRI for 9 lesions. Imaging features were classified according to the BI-RADS 5th edition lexicon. Images were reviewed by two radiologists, and consensus was obtained regarding imaging features. AI DS was retrospectively applied to the breast masses identified on ultrasound. Data analysis was performed using descriptive statistics. Results: 17 females and 1 male were included in this study. Mammographic findings varied from solitary masses (3/13 [23.1%]), asymmetries (3/13 [23.1%]), architectural distortion (1/13 [7.7%]), skin thickening (3/13 [23.1%]), focal asymmetry with calcifications (1/13 [7.7%]), or no suspicious findings (2/13 [15.4%]). Sonography often revealed masses with an irregular shape (13/16 [81.2%]), non-circumscribed margins (15/16 [93.7%]), hypoechoic echo pattern (10/16 [62.5%]), and vascular flow (12/16 [75%]). MRI showed heterogeneously enhancing masses (6/9 [66.7%]) or isolated skin enhancement (3/9 [33.3%]). AI DS analyzed 16 masses on ultrasound and identified 15 (93.8%) as suspicious. Conclusions: Breast sarcomas had a variable appearance on breast imaging, ranging from a solitary mass to isolated skin findings. Awareness of how breast sarcomas can present across imaging modalities while using AI DS as an aid may help radiologists in making the correct diagnosis of this rare and aggressive disease. Full article

Background: Gait event detection from inertial sensors offers scalable insights into locomotor health, with applications in clinical monitoring and mobile health. However, supervised methods are limited by scarce annotations, device variability, and sensor placement shifts. This in silico study evaluates self-supervised learning (SSL) as a resource-efficient strategy to improve robustness and generalizability. Methods: Six public smartphone and wearable inertial measurements unit (IMU) datasets (WISDM, PAMAP2, KU-HAR, mHealth, OPPORTUNITY, RWHAR) were harmonized within a unified deep learning pipeline. Models were pretrained on unlabeled windows using contrastive SSL with sensor-aware augmentations, then fine-tuned with varying label fractions. Experiments systematically assessed included (1) pretraining scale, (2) label efficiency, (3) augmentation contributions, (4) device/placement shifts, (5) sampling-rate sensitivity, and (6) backbone comparisons (CNN, TCN, BiLSTM, Transformer). Results: SSL consistently outperformed supervised baselines. Pretraining yielded accuracy gains of ΔF1 +0.08–0.15 and reduced stride-time error by −8 to −12 ms. SSL cut label needs by up to 95%, achieving competitive performance with only 5–10% labeled data. Sensor-aware augmentations, particularly axis-swap and drift, drove the strongest transfer gains. Robustness was maintained across sampling rates (25–100 Hz) and device/placement shifts. CNNs and TCNs offered the best efficiency–accuracy trade-offs, while Transformers delivered the highest accuracy at greater cost. Conclusions: This computational analysis across six datasets shows SSL enhances gait event detection with improved accuracy, efficiency, and robustness under minimal supervision, establishing a scalable framework for human performance and sports medicine in clinical and mobile health applications. Full article

Background/Objectives: Precise breast ultrasound (BUS) segmentation supports reliable measurement, quantitative analysis, and downstream classification yet remains difficult for small or low-contrast lesions with fuzzy margins and speckle noise. Text prompts can add clinical context, but directly applying weakly localized text–image cues (e.g., CAM/CLIP-derived signals) tends to produce coarse, blob-like responses that smear boundaries unless additional mechanisms recover fine edges. Methods: We propose XBusNet, a novel dual-prompt, dual-branch multimodal model that combines image features with clinically grounded text. A global pathway based on a CLIP Vision Transformer encodes whole-image semantics conditioned on lesion size and location, while a local U-Net pathway emphasizes precise boundaries and is modulated by prompts that describe shape, margin, and Breast Imaging Reporting and Data System (BI-RADS) terms. Prompts are assembled automatically from structured metadata, requiring no manual clicks. We evaluate the model on the Breast Lesions USG (BLU) dataset using five-fold cross-validation. The primary metrics are Dice and Intersection over Union (IoU); we also conduct size-stratified analyses and ablations to assess the roles of the global and local paths and the text-driven modulation. Results: XBusNet achieves state-of-the-art performance on BLU, with a mean Dice of 0.8766 and IoU of 0.8150, outperforming six strong baselines. Small lesions show the largest gains, with fewer missed regions and fewer spurious activations. Ablation studies show complementary contributions of global context, local boundary modeling, and prompt-based modulation. Conclusions: A dual-prompt, dual-branch multimodal design that merges global semantics with local precision yields accurate BUS segmentation masks and improves robustness for small, low-contrast lesions. Full article

Biofilm growth on silicone (Si) medical devices is routinely treated with antibiotics or device removal; however, new approaches are needed. The current work evaluates photothermal therapy (PTT) to augment antibiotic efficacy or directly ablate Staphylococcus aureus biofilms. The semiconducting polymer, Poly [4,4-bis(2-ethylhexyl)-cyclopenta [2,1-b;3,4 b’]dithiophene-2,6-diyl-alt22,1,3-benzoselenadiazole-4,7-diyl] (PCPDTBSe), with a high photothermal conversion efficiency of 53.2%, was formulated into nanoparticles (BSe NPs) and incorporated into Si. Nanocomposites were stimulated with 800 nm light to generate mild hyperthermic conditions of 42 °C, or ablative temperatures above 50 °C. PTT, with or without antibiotics, was deployed against two strains of Staphylococcus aureus biofilms, Xen 29 and Xen 40, followed by an evaluation of bacterial survival, biofilm regrowth, and differential disruption of specific biofilm components. Mild hyperthermia was also used in an in vivo model of silicone implant infection. The results demonstrate a 55–59% reduction in S. aureus when PTT plus antibiotic was used in vitro, and a 51% reduction in vivo. Higher temperatures effectively eradicate both Xen 29 and Xen 40 strains, with a longer exposure time using lower laser power being optimal. Hyperthermia inhibited biofilm regrowth in both strains, resulting in a > 3 log reduction, plus increased dead cells, polysaccharides, and eDNA in treated Xen 40 biofilms. These experiments demonstrate that nanocomposite-based PTT can both reduce viable bacteria and alter individual biofilm components. Full article

The performance of dental composites is strongly dependent on the type and content of ceramic fillers incorporated into the resin matrix. In this study, the effect of nanosilica (NS) fillers on the curing kinetics, physicochemical, thermal, and mechanical properties of Bis-GMA/TEGDMA-based dental composites was systematically investigated. A series of nanocomposites containing various weight fractions of NS was prepared and evaluated. The photocuring behaviour was analysed using differential scanning calorimetry (DSC), enabling the determination of polymerisation rate coefficients (propagation k_p and bimolecular termination k_t^b) and double bond conversion. The presence of nanosilica was found to influence chain mobility, as evidenced by changes in glass transition temperature (T_g). Rheological measurements provided insight into viscosity changes induced by NS incorporation, while mechanical tests confirmed reinforcement effects. A moderate but statistically significant correlation was observed between the NS content and mechanical performance. The results obtained correlate the rheological, kinetic, thermal, and mechanical properties of multiple types of silica in a single resin system using a consistent methodology. In addition, the results highlight the role of nanosilica in the regulation of the curing dynamics and the increase in the mechanical integrity of methacrylate-based dental composites, representing a promising strategy for the development of next-generation restorative materials. Full article

Sensor sampling errors and inverter dead-time effects introduce significant nonlinear disturbances into dual three-phase permanent magnet synchronous machine (DTP-PMSM) drive systems with sinusoidal excitation, leading to pronounced alternating current (AC) and direct current (DC) disturbances. These disturbances severely compromise the stability and reliability of the current control loop, ultimately degrading the overall driving accuracy of the system. To effectively address this issue, this paper proposes a novel interference suppression strategy based on bi-subspace predictive current control. Specifically, the proposed approach optimizes modulation through two-step virtual-vector-based predictive current control (VVPCC) operation to achieve disturbance decoupling. Building upon this foundation, a model-assisted discrete extended state observer (DESO) is incorporated into the fundamental subspace, whereas a discrete vector resonant controller (DVRC) with pre-distorted Tustin discretization is applied to the secondary subspace. Modeling analysis and experimental results demonstrate that, compared with the classical VVPCC method, the proposed bi-subspace VVPCC method has good steady-state performance and enhanced robustness in the presence of disturbances. Full article

Background and Objectives: Diffuse large B-cell lymphomas (DLBCLs) are heterogeneous neoplasms. CD79B and MYD88 mutations are associated with the activated B-cell-like (ABC) subtype of DLBCL and often co-occur and lead to constitutive activation of the NF-κB pathway. Several different genetic classifications to date have recognized CD79B- and MYD88-mutated DLBCLs as a unique subtype with poor response to therapy and unfavorable survival. However, little is known about gene expression in DLBCLs with mutated CD79B (and MYD88) in comparison to their wild type counterparts. The objective of this study was to compare the gene expression in DLBCLs according to their CD79B mutational status. Methods: A total of 48 primary, treatment-naïve DLBCLs (CD79B-mutated: 35%/n = 17, CD79B-wild type: 65%/n = 31) were investigated using RNA expression profiling (770 genes), followed by immunohistochemical analysis of the up-regulated genes and survival analysis. Results: The gene expression analysis revealed that downstream of CD79B CARD11 and the NF-κB targets NFKBIZ, IL10, IL12A, PIM1 and BCL2A1 were up-regulated in CD79B-mutated DLBCLs. The strongest up-regulation was detected for ARNT2 and WNT11. Other up-regulated genes included the apoptosis-related BID and granzyme B, as well as genes of cell cycle regulation such as RUNX1, RUNX1T1 and RASGRF1. Up-regulation was also found for IL7, STAT3, MLLT4, CD14 and the HSP90B1 subunit. TP53 mutation showed an association with poorer overall survival in a secondary analysis, consistent with prior reports, while survival by CD79B/MYD88 mutation status and the differentially expressed genes showed no significant differences in this cohort. Conclusions: In conclusion, the current study identified novel up-regulated genes in CD79B-mutated DLBCLs beyond NF-κB pathway signaling, which may contribute to a better definition of potential therapeutic targets and further improves the characterization of this distinct and aggressive DLBCL subgroup. Full article

Chromosome 6q deletion syndrome is a rare entity that has a highly variable clinical presentation and size of deletions. The most frequent manifestations of 6q terminal deletion are intellectual disability, facial dysmorphism, brain structural anomalies, and congenital heart defects. The phenotype is not clinically recognizable, except in those who harbor a terminal 6q deletion that includes the ARID1B gene, in whom features similar to Coffin–Siris syndrome (CSS) can be observed. We report the case of a female newborn with a prenatal diagnosis of a terminal deletion on 6q25.1q27, which encompasses the ARID1B gene, and who was diagnosed with CSS during the neonatal period. From our review, we found that facial gestalt, hypertrichosis, and fifth fingernail aplasia/hypoplasia, along with other features, such as vertebral defects and cystic hygroma (or webbed neck), correlated with the presence of a CSS causally related to 6q25.3 small deletions that include the ARID1B gene. Full article

Reactions of anhydrous Zn(II) iodides with redox-active 1,4-diaza-1,3-butadiene (DAD) and its bis(imino)acenaphtene (BIAN) derivatives in absolute acetonitrile yielded a series of new complexes: [(Mes-DAD)ZnI₂] (1), [(dpp-DAD)ZnI₂] (2), and [(dpp-BIAN)ZnI₂] (3). Single crystals of all compounds were obtained, and their molecular structures were unambiguously determined by X-ray diffraction analysis. Purity of bulk samples in solid state was confirmed by PXRD. Stability of the complexes in solution was investigated by means of UV-Vis and NMR spectroscopy. Cyclic voltammetry revealed two or three quasi-reversible reduction waves in the cathodic region for complexes 1–3. The ability of 3 to accept up to three electrons highlights the potential of these compounds as electrocatalysts for reductive transformations. Full article

To address information isolation and incomplete parameter extraction among multi-modal data (e.g., drawings, text, and tables) in the operation and maintenance stage of buildings, this paper proposes a multi-modal data parsing, automatic parameter extraction, and standardized integration method oriented toward 3D modeling. First, by employing vector element parsing and layer semantic analysis, the method enables structured extraction of key component geometry from architectural drawings and improves modeling accuracy via spatial topological relationship analysis. Second, by combining regular expressions, a domain-specific terminology dictionary, and a BiLSTM-CRF deep learning model, the extraction accuracy of unstructured parameters from architectural texts is significantly improved. Third, a multi-scale sliding window and geometric feature analysis are used to achieve automatic detection and parameter extraction from complex nested tables. Regarding the experimental setup: the drawings consist of a large-scale collection of DXF files stratified and randomly split into train/val/test with an approximate 8:1:1 ratio; the text set includes 1550 PDF-derived specification fragments (8:1:1 split); and the tables cover typical door/window, structural, and electrical schedules (also split ~8:1:1). F1 scores use micro-F1 (instance-level aggregation), and 95% confidence intervals and their computation are described in the main text. Experimental results show that the F1 scores for wall line, wall, and column recognition reach 98.1%, 84.9%, and 92.2%, respectively, while the F1 scores for door and window recognition are 74.3% and 76.2%. For text parameter extraction, the proposed PENet model achieves a precision of 83.56% and a recall of 86.91%. For the table task, the parameter extraction recalls for doors/windows and structure are 95.0% and 96.7%, respectively. The proposed method enables efficient parameter extraction and standardization from multi-modal architectural data, demonstrates significant advantages in handling heterogeneous data and improving modeling efficiency, and provides practical technical support for the digital reconstruction and intelligent management of existing buildings. Full article

The synthesis of a small library of fused Cyclic Aryl Amino Carbon (f-CArAC) carbene precursors in the form of 1,1,2,4-tetraaryl-1H-isoindol-2-ium triflate (6), (7-R) (R = ^tBu, CF₃) or 3,3-dimethyl-2,8-bis-arene-substituted-3,4-dihydro-isoquinolin-2-ium hydrogen-dichloride (8) and 2,4,8-tri(substituted)-isoquinolin-2-ium tosylate salts (12) has been achieved. All of them feature an arene incorporated on the annulated benzene ring of the corresponding heterocycle, introduced at the early stages of their synthesis via the Suzuki cross-coupling reaction between 2,6-dibromo-benzaldehyde and the desired aryl boronic acid. The terphenyl-2′carbaldehyde by-products of this Suzuki reaction are useful starting points for the preparation of two new iminium iodide salts (10-R) (R = H, CF₃) as potential precursors to access ACyclic Amino Carbon (ACAC) carbenes. Compounds (6) and (7-^tBu) react readily with hydroxide either in THF or in a biphasic Et₂O/aqueous OH⁻ solution to produce the substituted isoindolinols (13) and (14), respectively. The thermal dehydration of the former generates the corresponding f-CArAC carbene in situ, which is trapped by Cu(I)Cl furnishing, a rare example of a two-coordinate Cu(I) complex (15) supported by this new ligand scaffold. Full article

Understanding genotype × environment (G × E) interaction is essential for the improvement of aromatic crops such as basil (Ocimum basilicum), where yield is strongly influenced by environmental variability. In this study, five basil varieties (Burns Lemon, Cinnamon, Sweet, Red Rubin, and Thai) were evaluated across two years (2015–2016, 2016–2017) and three irrigation levels (40%, 70%, and 100% of the full water requirement) to assess dry biomass yield. ANOVA and mean performance plots confirmed significant varietal differences (F = 33.972, p < 0.001) and substantial Y × V interaction (F = 23.578, p < 0.001), motivating a deeper exploration of association patterns. To this end, we proposed the use of a modified version of Simple Correspondence Analysis (CA) combined with three variations of bi-plot analyses in order to explore the (G × E) interaction. In addition, another modification of CA is proposed and used, CA of raw data (CA-raw), for the same reason. For the purpose of the study, the combinations of the two cultivation periods (years) by the three irrigation levels were considered as six environments. Results showed that the proposed modification of CA of raw data serves as a faithful baseline for the study of (G × E) interaction. On the other hand, the proposed modified version of simple CA, after proper normalization (row, column, symmetrical, principal) of the factorial scores of the five basil varieties and the six environments, provide insights depending on whether the research focus lies on varieties, environments, or their joint associations (interaction). Overall, the combined use of ANOVA, mean plots, and CA under multiple normalizations and modifications demonstrated the robustness of the primary varietal–environment contrast, while also showing how methodological choices shape interpretation. The proposed methods are “model free” and can be used also with secondary published data. Full article