Search Results (497)

Hypersaline culture media used for cultivation of Dunaliella salina represent complex multicomponent aqueous systems whose cooling–heating phase behavior remains insufficiently characterized. In this study, the thermal transitions of two biologically relevant hypersaline media (Artari and Ramaraj) were investigated using differential scanning calorimetry (DSC) and cryomicroscopy. The media were examined at NaCl concentrations of 1.5, 2.0, and 4.0 M, corresponding to moderate to highly concentrated brine conditions comparable to natural salt lakes and evaporative basins. DSC analysis revealed pronounced salinity-dependent suppression of ice crystallization and modification of melting transitions relative to classical NaCl–water systems. Increased NaCl concentration reduced recrystallization during heating and shifted peak temperatures, indicating kinetic and compositional effects in the unfrozen fraction. Rapid cooling promoted formation of partially amorphous phases, consistent with limited vitrification in highly concentrated media. Cryomicroscopy directly confirmed changes in ice morphology, nucleation density, and crystal growth dynamics under varying salinity and thermal histories. The combined calorimetric and microscopic approach demonstrates that complete hypersaline cultivation media exhibit phase behavior that cannot be fully extrapolated from simplified binary systems. These findings provide new insight into the physicochemical stability of multicomponent brines during cooling and highlight the critical role of salinity and thermal history in controlling crystallization pathways in hypersaline aqueous environments. Full article

Background: Advanced liver fibrosis (LF) is a major determinant of prognosis across chronic liver diseases. Current biomarkers are often etiology-specific and lack cross-cohort robustness. Shared molecular drivers across etiologies remain incompletely defined, and effective anti-fibrotic therapies are limited. Methods: We developed a multi-algorithm consensus machine-learning framework to derive a robust LF progression signature. In the training non-alcoholic fatty liver disease (NAFLD) cohort GSE213621 (n = 368), samples were formulated as a binary classification task (mild fibrosis, F0–F2; advanced fibrosis, F3–F4). Candidate genes were screened in parallel using Boruta, Least Absolute Shrinkage and Selection Operator (LASSO), random forest, and eXtreme Gradient Boosting (XGBoost). Genes selected by at least two algorithms were defined as a high-consensus pool, and genes consistently selected by all four algorithms were prioritized to construct a core signature. Model performance was evaluated by stratified cross-validation in the training cohort and externally validated in four independent cohorts of different etiologies (GSE49541, GSE84044, GSE130970, and GSE276114). Cellular sources of signature genes were characterized using single-cell RNA sequencing (scRNA-seq) datasets GSE136103 (human) and GSE172492 (mouse). For therapeutic discovery, the high-consensus expression profile was queried against the Connectivity Map (CMap) to prioritize compounds predicted to reverse the fibrotic transcriptional program. Withaferin A (WFA) was selected for experimental validation in a carbon tetrachloride (CCl₄)-induced mouse LF model and in the transforming growth factor-β1 (TGF-β1)-stimulated human hepatic stellate cell line LX-2. Bulk liver RNA-seq profiling was performed to interrogate WFA-associated molecular changes in vivo. Results: We identified a six-gene signature (CLEC4M, COL25A1, ITGBL1, NALCN, PAPPA, and PEG3) that discriminated advanced from mild fibrosis, achieving a mean AUC of 0.890 in internal cross-validation and an average AUC of 0.864 across external validation cohorts. scRNA-seq analysis revealed cell-type-specific expression with prominent enrichment in fibroblast populations. In vivo, WFA markedly attenuated CCl₄-induced fibrosis (p < 0.05) and reversed 1314 fibrosis-associated differentially expressed genes (adjusted p < 0.05), which were enriched in fatty acid metabolism and PPAR signaling, as well as extracellular matrix (ECM)–receptor interaction and focal adhesion (adjusted p < 0.05). In vitro, WFA suppressed TGF-β1-induced LX-2 activation, reducing α-SMA and Fibronectin expression (p < 0.05). Conclusions: We report a six-gene signature that robustly predicts advanced LF across etiologies, define its cellular context using single-cell atlases, and validate the anti-fibrotic activity of WFA in both in vivo and in vitro models. Bulk liver RNA-seq and cellular evidence further suggest that WFA-associated effects are linked to lipid metabolic programs, ECM remodeling, and attenuation of hepatic stellate cell activation. Full article

Blended nanocomposite solid polymer electrolytes are gaining considerable attention as next-generation materials for use in flexible lithium-ion battery systems. These materials help ensure a more uniform distribution of lithium ions at the electrode–electrolyte interface, contributing to the development of a stable interfacial layer that mitigates lithium dendrite formation. In this study, solid polymer electrolytes were synthesized using a binary polymer matrix composed of polyethylene oxide (PEO) and polymethyl methacrylate (PMMA), with lithium iodide (LiI) as the ionic salt. Zirconium dioxide (ZrO₂) nanoparticles were introduced as nanofillers in varying concentrations to investigate their influence on the physical and functional characteristics of the polymer matrix. Characterization was carried out using Scanning Electron Microscopy (SEM), Fourier Transform Infrared Spectroscopy (FTIR), and X-ray Diffraction (XRD). SEM images indicated that ZrO₂ nanoparticles remained well-dispersed up to 3 wt%, while higher loadings showed slight agglomeration. FTIR analysis revealed noticeable changes in absorption bands, suggesting strong interactions among polymer chains and the nanofillers. XRD data confirmed the semi-crystalline behavior of the PEO/PMMA blend system. The inclusion of ZrO₂ nanofillers enhanced the structural integrity and ionic conductivity of the polymer matrix, making them promising candidates for applications in electrochemical energy storage and advanced material interfaces. The systematic incorporation of ZrO₂ nanofillers into the PEO/PMMA matrix significantly improved the microstructural uniformity, polymer–filler interactions, and ionic transport behavior of the solid polymer electrolytes. Full article

This study reconstructs the geography of meaning of the German perception verb schmecken on the basis of 30 major dialect dictionaries, treating them as a distributed semantic corpus and coding attestations as binary variables reflecting the presence or absence of semantic options. Combining a construal-based framework with spatial modeling, the analysis shows that the polysemy of schmecken is structured by three mutually reinforcing forces: embodied sensory organization, construal-based perspectivization, and regionally patterned areal dynamics. The gustatory–olfactory axis forms the semantic core of the verb, from which tactile, visual, affective, and epistemic extensions emerge. These extensions align with systematic pathways constrained by agentive, experiential, emissive, and evaluative construals, demonstrating that semantic extension is channeled through specific construal modes—notably emissive and agentive—rather than determined by sensory modality alone. A detailed areal analysis reveals a pronounced north–south divide. While Low German dialects conform to the cross-linguistically more common tendency to avoid colexifying taste and smekk—itself the outcome of historical change rather than uninterrupted differentiation—Upper German varieties preserve a typologically rare gustatory–olfactory cluster and exhibit the richest range of cross-modal and abstract extensions. The resulting semantic graph formalizes how regional varieties activate different subsets of a lexeme’s semantic potential and demonstrates that semantic networks themselves display spatial organization. The study thus provides an empirically grounded reconstruction of a German geography of meaning and illustrates how dialect data illuminate the interplay between embodied cognition, construal-based lexical architecture, and areal dynamics. Full article

Chronic lymphocytic leukemia (CLL) is uncommon in Asia, and longitudinal genomic data from Asian cohorts are limited. We conducted serial whole-exome sequencing (WES) in a multicenter Korean cohort of newly diagnosed, elderly CLL treated with chlorambucil–obinutuzumab to evaluate mutational heterogeneity and clonal hematopoiesis of indeterminate potential (CHIP) during treatment and follow-up. Tumor-only variants were filtered, restricted to nonsynonymous or loss-of-function coding/splice-site mutations, and summarized as a binary patient-by-gene matrix for principal component analysis (PCA), trajectory analysis, and k-means clustering. CHIP was defined as ≥1 qualifying mutation in a prespecified CHIP gene set. Baseline PCA was more compact in patients with complete response at end of treatment, whereas partial response or progressive disease cases were more dispersed. PCA trajectories were compact and directionally consistent in complete responders, more dispersed in partial responders, and highly heterogeneous without a dominant direction in progressive disease. Clustering identified dispersed and compact clusters, and CHIP-associated mutations were enriched in the dispersed cluster (55.6% vs. 8.3%, Fisher’s exact p = 0.0086). In paired samples collected 3–5 months after end of treatment, CHIP status changed in some patients. Serial WES may provide complementary information to treatment response, although these observations require confirmation in larger cohorts. Full article

Background/Objectives: Biopsy examination remains the gold standard for cancer diagnosis, relying on histopathological assessment of tissue samples to identify malignant changes. However, manual interpretation of histopathological slides is time-consuming, subjective, and susceptible to inter-observer variability. The digitization of histopathological images enables automated analysis and offers opportunities to support clinicians with more consistent and objective diagnostic tools. This study aims to enhance cancer diagnosis by proposing a hybrid framework that integrates deep-learning-based histopathological image analysis with Whispering Gallery Mode (WGM) optical sensing for complementary tissue characterization. Methods: The proposed framework combines automated tumor classification from histopathological images with biochemical signal analysis obtained from WGM optical sensors. Deep learning models, including EfficientNet-B0, InceptionV3, and Vision Transformer (ViT), were employed for binary and multi-class tumor classification using the BreakHis dataset. To address class imbalance, a Deep Convolutional Generative Adversarial Network (DCGAN) was utilized to generate synthetic histopathological images alongside conventional data augmentation techniques. In parallel, WGM optical sensors were incorporated to capture subtle tissue-specific signatures, with machine learning algorithms enabling automated feature extraction and classification of the acquired signals. Results: In multi-class classification, InceptionV3 combined with DCGAN-based augmentation achieved an accuracy of 94.45%, while binary classification reached 96.49%. Fine-tuned Vision Transformer models achieved a higher classification accuracy of 98% on the BreakHis dataset. The integration of WGM optical sensing provided additional biochemical information, offering complementary insights to image-based analysis and supporting more robust diagnostic decision-making. Conclusions: The proposed hybrid framework demonstrates the potential of combining deep-learning-based histopathological image analysis with WGM optical sensing to improve the accuracy and reliability of cancer classification. By integrating morphological and biochemical information, the framework offers a promising approach for enhanced, objective, and supportive cancer diagnostic systems. Full article

The effective management of municipal road infrastructure requires up-to-date, standardized and reliable condition information to support sustainable maintenance. While visual road-condition assessment methods based on established standards are widely applied to municipal roads, they remain largely manual, time-consuming, costly and subjective. This study presents an end-to-end workflow for the automated visual inspection and condition assessment of municipal road infrastructure using high-resolution, 3D street-level imagery acquired by professional mobile mapping systems. The proposed approach integrates an efficient preprocessing pipeline for precise road-surface extraction with deep learning models trained for the specific task and an advanced postprocessing method for robust results aggregation. For this purpose, a large dataset covering approximately 352 km of municipal roads across eight municipalities was created by combining street-level imagery with expert-annotated road-condition index (RCI) values. Two neural network variants were implemented: a regression model predicting standardized RCI values and a binary classifier distinguishing between roads requiring maintenance and those in good condition. To ensure decision-oriented outputs at the infrastructure-asset level, frame-based predictions are aggregated into homogeneous road segments using outlier detection and change-point analysis along the road axis. The regression model achieved a mean absolute error of 0.48 RCI values at frame level and 0.40 RCI values at road-segment level, outperforming conventional inter-expert variability, while the binary classification model reached an F1-score of 0.85. These findings demonstrate that AI-based visual road-condition assessment using professional mobile mapping data can provide accurate, standardized and scalable condition information for municipal road infrastructure. The proposed workflow supports maintenance prioritization and infrastructure management decisions without requiring explicit detection of individual pavement defects, offering a practical pathway toward automated, cost-effective road-condition monitoring. Full article

Background: The COVID-19 pandemic revealed persistent gaps in local health department (LHD) health informatics capacity. This study examines organizational characteristics of LHDs associated with the adoption of six health information systems: electronic case reporting (eCR), electronic disease reporting systems (EDRS), electronic health records (EHR), electronic lab reporting (ELR), health information exchange (HIE), and immunization registries (IR). Methods: We used a mixed-methods design, including multinomial or binary logistic regression analyses of quantitative data from the 2022 NACCHO National Profile of Local Health Departments (n = 441) and thematic analysis of semi-structured interviews with five LHD staff members. Results: About half (49.9%) of LHDs had implemented eCR, while higher proportions had implemented EDRS (78.0%), EHR (62.4%), ELR (57.2%), HIE (92.6%), and IR (92.6%). Workforce size was associated with the implementation of eCR, EHR, and IR. The number of vacant staff positions was associated with a lower odds of IR implementation; compared with medium-sized LHDs, both small and large LHDs had higher odds of IR implementation. Shared-governance LHDs had higher odds of adopting ELR and HIE than state-governed LHDs. Qualitative themes highlighted challenges, including staff burnout, high turnover, pay inequities, role ambiguity, political pressures, rapid changes in informatics, and interoperability problems. Conclusions: Findings underscore the need to improve LHD workforce capacity and governance structures to support a resilient public health informatics infrastructure. Full article

Wafer map defect classification plays a critical role in yield monitoring and root-cause analysis in semiconductor manufacturing. Although recent convolutional neural network (CNN)-based approaches have achieved high classification accuracy, most existing models are evaluated primarily on clean datasets and remain vulnerable to unseen perturbations and representation-level variability at test time. In this paper, we propose a hybrid CNN–echo state network (ESN) architecture that integrates spatial feature extraction with sequential aggregation to enhance robustness under input perturbations. The CNN backbone extracts two-dimensional feature maps, which are converted into ordered sequences using a multidirectional scanline strategy and processed by an ESN reservoir. The resulting sequential representations are combined with CNN features through a class-specific adaptive fusion mechanism. Using the defect-only eight-class version of the WM-811K dataset, we systematically evaluate robustness under multiple perturbation scenarios, with particular focus on the clean train/noisy test (CT-NT) setting. To ensure a controlled robustness evaluation aligned with the binary nature of wafer map data, we introduce binary-consistent die-flip perturbations and additionally employ additive Gaussian perturbations as a representation-level stress test. Under clean-data conditions, the proposed model showed a 0.61 pp improvement in test accuracy compared to the ResNet34-based CNN, with notably larger gains for rare classes and defect types exhibiting strong structural patterns. In the clean train/noisy test scenario, where the model was trained on clean wafer map data and evaluated under controlled test-time perturbations, the accuracy of the CNN baseline dropped to 77.59% at $\sigma$ = 0.10, whereas the proposed hybrid model maintained an accuracy of 87.30%, resulting in an absolute improvement of 9.71 pp. Per-class analysis reveals that the robustness gain is class-dependent, with pronounced improvements for defect types exhibiting clear and repetitive structural patterns, such as Loc and Edge-Ring. Further mechanistic analysis demonstrates that the robustness improvement arises from enhanced representation stability and bounded reservoir dynamics, rather than from changes in CNN feature extraction or training regularization. These results demonstrate that the proposed CNN-ESN hybrid architecture provides meaningful advantages in terms of robustness under noisy evaluation conditions without requiring noise-aware training or prior knowledge of perturbation characteristics. Full article

Background/Objectives: The human larynx exhibits marked sexual dimorphism and undergoes age-related structural remodeling, both of which influence voice characteristics and have important implications for diagnostic assessment. While sex-related differences in laryngeal size are well recognized, the extent to which aging contributes to dimensional versus qualitative structural changes remains incompletely defined. This study aimed to analyze sex- and age-related morphometric and histological characteristics of the human larynx, with a focus on features relevant to voice evaluation and diagnostic interpretation. Methods: A cross-sectional anatomical study was conducted on 80 cadaveric human larynges preserved in 10% buffered formalin. Specimens were stratified by sex and age (<30, 30–60, and ≥60 years). Direct morphometric measurements included anteroposterior laryngeal length, thyroid cartilage height, thyroid angle, and relative glottic area. Epiglottic morphology and the presence of laryngeal cartilage calcification/ossification (binary classification: present vs. absent) were recorded. Histological analysis of vocal fold tissue was performed on a stratified subset of specimens. Statistical analysis included t-tests, chi-square tests, two-way ANOVA, effect size estimation, and logistic regression. Results: Male specimens showed significantly greater anteroposterior length, thyroid cartilage height, and relative glottic area, along with a narrower thyroid angle, compared with females (all p < 0.001), with large effect sizes. Age did not significantly influence overall laryngeal dimensions. In contrast, cartilage calcification/ossification increased markedly after the age of 60. Logistic regression identified age ≥ 60 years as the only independent predictor of calcification (OR = 4.37, p = 0.039), while sex was not significant. Epiglottic morphology demonstrated a sex-dependent distribution. Histology revealed age-related muscle atrophy and reduced collagen and elastin density. Conclusions: Sex defines the baseline morphometric framework of the adult larynx, whereas aging, particularly beyond 60 years, drives qualitative structural degeneration. These findings provide a reproducible anatomical reference for distinguishing sex-related variation from age-related changes in diagnostic assessment. Full article

Neonicotinoid insecticides and fluoroquinolone antibiotics frequently co-occur in aquatic and terrestrial environments, posing a threat to human health, yet their combined neurotoxic potential remains poorly characterized. This study aimed to assess the cytotoxicity of typical neonicotinoids and fluoroquinolones as well as their mixtures in human neuroblastoma SK-N-SH cells and identify affected pathways. SK-N-SH cells were exposed to clothianidin (CLO), imidacloprid (IMI), enrofloxacin (ENR), and ofloxacin (OFX) individually and in fixed-ratio mixtures (50% of each compound’s IC₅₀) for 24 h and 48 h, and cell viability was quantified using the alamarBlue^® method. Single-compound dose–response testing showed time-dependent cytotoxicity, with higher potency for fluoroquinolones (24 h IC₅₀: ENR 1.446 mM, OFX 2.742 mM; 48 h IC₅₀: ENR 0.826 mM, OFX 2.005 mM) than neonicotinoids (24 h IC₅₀: IMI 4.754 mM, CLO 5.356 mM; 48 h IC₅₀: IMI 3.631 mM, CLO 4.029 mM). Concentration-addition analysis indicated that most mixtures produced synergistic interaction in reduction in cell viability, with ENR+OFX showing the strongest effect at 48 h (Observed viability 7.138% vs. Predicated viability 82.368%). RNA-seq (24 h) revealed that binary mixtures generally induced more differentially expressed genes than single exposures, and ENR-containing mixtures showed the largest transcriptomic shifts, enriching pathways related to cellular stress and injury as well as neuronal signaling and connectivity. RT-qPCR validated the changes in expressions of five key neurobiology-relevant genes (LMO3, NOS1, ADCY8, FGF7 and TNFRSF12A). These findings highlight the importance of assessing insecticide–antibiotic mixtures when evaluating their hazards in environment. Full article

Purpose: To develop prognostic models integrating delta radiomics from prostate-specific membrane antigen positron emission tomography/computed tomography (PSMA-PET/CT) and dosiomics with clinical variables to predict metastasis-free survival (MFS) in patients with localized prostate adenocarcinoma treated with androgen deprivation therapy and external-beam radiotherapy. Materials/Methods: Delta-radiomics analysis included 43 patients. Radiomics features were extracted from the primary tumor on pre- and post-treatment PSMA-PET/CT, and delta features were calculated as relative changes. Eight high-variance features were selected and combined with clinical variables (age, Gleason score, initial PSA, and a binary variable, indicating the occurrence of PSA relapse). Data was split 70:30 with training-set imbalance correction. Predictors that were significant in univariate Cox regression (p < 0.05) were entered into multivariate Cox models, and five-year MFS was classified using a quadratic support vector machine. Dosiomics analysis included 48 patients. Dosiomics features were extracted from the planning target volume receiving 86 Gy and combined with pre-treatment radiomics and clinical variables using the same framework. Results: For delta radiomics, Model 1 (delta radiomics + pre-treatment radiomics + clinical) achieved the best performance (test c-score 0.58; AUC 0.70), exceeding Model 2 (pre-treatment radiomics + clinical; c-score 0.56; AUC 0.65) and Model 3 (clinical only; c-score 0.51; AUC 0.56). For dosiomics, Model 1 showed the highest performance (test c-score 0.56; AUC 0.67) compared with Model 2 (c-score 0.55; AUC 0.62) and Model 3 (c-score 0.50; AUC 0.54). Conclusions: Integrating delta radiomics or dosiomics with pre-treatment imaging and clinical variables improves MFS prediction and supports their role as non-invasive biomarkers for individualized radiotherapy in localized prostate cancer. Full article

Morocco is increasingly vulnerable to climate change, as reflected by recurrent droughts and rising soil and groundwater salinization, which threaten staple crops and rural livelihoods. In this context, the introduction of drought- and salinity-tolerant crops such as quinoa represents a strategic option for enhancing agricultural resilience and supporting sustainable rural development. This study analyzes quinoa adoption in two contrasting Moroccan regions, Rehamna and the Oriental, with the aim of determining key socio-economic, institutional, and environmental drivers. Field surveys were conducted to collect data on farmers’ personal characteristics, farm attributes, and access to resources related to quinoa cultivation, including water, information, and credit. Data analysis combined descriptive statistics, a binary logistic regression model (Logit), Factorial Analysis for Mixed Data (FAMD), and Hierarchical Cluster Analysis (HCPC) to identify adoption determinants and explore heterogeneity among farmers. The results reveal both common factors and region-specific dynamics shaping quinoa adoption. Cooperative membership emerges as a central determinant in both regions, facilitating access to information, collective learning, and market integration, with a stronger effect observed in the Oriental region. Water scarcity appears as a critical constraint, particularly in Rehamna. Adoption pathways also differ across regions, with a higher prevalence of direct adoption among farmers in the Oriental. Interpreted through the lens of innovation diffusion and multidimensional sustainability, the findings show that quinoa adoption is not merely a technical choice but a socio-economic adaptation strategy. Quinoa should therefore be considered a complementary crop within diversified farming systems, contributing to environmental resilience, income diversification, and social inclusion. These results provide relevant insights for the design of policies aimed at promoting sustainable agricultural innovation in marginal environments. Full article

The accurate interpretation of land cover changes in multi-temporal satellite imagery is critical for Earth observation. However, existing methods typically yield static outputs—such as binary masks or fixed captions—lacking interactivity and user guidance. To address this limitation, we introduce remote sensing image change analysis (RSICA), a novel paradigm that enables the instruction-guided, multi-turn exploration of temporal differences in bi-temporal images through visual question answering. To realize RSICA, we propose DeltaVLM, a vision language model specifically designed for interactive change understanding. DeltaVLM comprises three key components: (1) a fine-tuned bi-temporal vision encoder that independently extracts semantic features from each image in the input pair; (2) a visual difference perception module with a cross-semantic relation measuring (CSRM) mechanism to interpret changes; and (3) an instruction-guided Q-former that selects query-relevant change features and aligns them with a frozen large language model to generate context-aware responses. We also present ChangeChat-105k, a large-scale instruction-following dataset containing over 105k diverse samples. Extensive experiments show that DeltaVLM achieves state-of-the-art performance in both single-turn captioning and multi-turn interactive change analysis, surpassing both general multimodal models and specialized remote sensing vision language models. Full article

Information security auditing plays an important role in information security management because it assesses the status of security mechanisms, risk management, and regulatory compliance. Most information security auditing methodologies have been based on binary assessments or checklists, an approach that is limited in the constant evolution of cyber threats. This paper presents a comparative analysis of the most recognized maturity level structures, such as the Capability Maturity Model Integration (CMMI), the Cybersecurity Capability Maturity Model (C2M2), and the Cybersecurity Maturity Model Certification (CMMC), in order to identify the most suitable one for an innovative change in the auditing process to obtain a deeper and more detailed evaluation of security controls and, consequently, better decision-making. Full article