Search Results (450)

This article addresses a current point of contention in the field of single-molecule/single-particle tracking, as well as the relevant literature, and supplements it with some published cell-based experiments to illustrate our conclusions and known theorems. We attempt to explain the controversy surrounding the differing biophysical and cell biological results of studies on the individual molecule and those “at the single-molecule level” as well as at the level of many molecules in such a way that even readers who are unfamiliar with the subject can understand it without having to read all the mathematical, physical, and biophysical references. Given this abundance of studies in the literature, it is obvious that genuine single-molecule studies are urgently needed, i.e., single-molecule studies that focus on increasing the sensitivity of the temporal resolution of single-molecule measurements and not just on spatial resolution. Full article

Chronic kidney disease (CKD) is a progressively worsening condition that erodes renal function over time, reduces quality of life, and can ultimately culminate in kidney failure with far-reaching systemic complications. In addition to reduced filtration, worsening kidney function disrupts mineral homeostasis and leads to CKD–mineral and bone disorder (CKD-MBD). Dysregulated calcium handling and maladaptive endocrine responses contribute to bone pathology and increase cardiovascular calcification risk; therefore, serial calcium monitoring remains clinically relevant for longitudinal CKD management. Conventional calcium measurements are typically obtained with centralized analyzers or laboratory assays (e.g., colorimetry and electrode/optical readouts). Despite high accuracy, the required instrumentation, controlled operating conditions, and pretreatment steps complicate rapid point-of-care deployment, especially when only microliter-scale biofluids are available. Accordingly, this study develops a finger-actuated microfluidic colorimetric platform capable of determining calcium ion concentrations in human biofluids, such as whole blood, serum, and urine. The platform integrates a three-dimensional PMMA/paper microchip with a compact reader that maintains stable temperature control while enabling CMOS-based optical detection. With just 6 μL of sample, a brief finger press propels the biofluid across an internal filtration layer, generating serum or cleaned urine that subsequently reacts with a pre-deposited murexide reagent. Under optimized conditions (1.6% reagent, 50 °C, 3 min), the signal follows a strong logarithmic relationship with calcium concentration (Y = 47.273 ln X + 28.890; R² = 0.9905), supporting quantification over 1–40 mg/dL and a detection limit of 0.2 mg/dL. Across 80 clinical CKD specimens spanning serum, whole blood, and urine, results aligned closely with the NM-BAPTA reference assay, with R² values exceeding 0.97. Full article

Lysine propionylation (Kpr) is a metabolically coupled lysine acylation that links propionyl-CoA availability to the molecular regulation of gene expression and protein function. Although lysine acetylation (Kac) is the most extensively characterized, recent proteomic and metabolic studies suggest that Kpr is more frequent than previously appreciated, occurs at defined lysine sites, and displays tissue-resolved and context-dependent patterns. Kpr often co-varies with other short-chain acylations such as Kac and lysine butyrylation (Kbu); however, emerging genomic-scale evidence indicates mark-biased genomic distributions and functional associations, suggesting that Kpr is not simply an extension or alternative to Kac. Notably, propionyl-CoA, the direct acyl donor for Kpr, can be influenced by microbiome-derived short-chain fatty acids (SCFAs), implying that interventions modulating SCFA availability (e.g., dietary manipulation) may provide an actionable route to tune Kpr and related acylations. Here, we summarize recent advances in propionyl-CoA sources and compartmentalization, the enzymatic writers/erasers/readers, the molecular mechanisms underlying Kpr, and the functional consequences of Kpr in physiology and disease. Full article

This study proposes a Natural Language Processing (NLP)-based evaluation framework to examine the linguistic consistency of large language model (LLM)-generated personalized texts over time. NLP metrics were used to quantify and compare linguistic patterns across repeated generations produced using identical prompts. In Experiment 1, internal reliability was examined across 10 repeated generations from four LLMs (Claude, Llama, Gemini, and ChatGPT), applied to 10 scientific texts tailored for a specific reader profile. Linear mixed-effects models showed no effect of repeated generation on linguistic features (e.g., cohesion, syntactic complexity, lexical sophistication), suggesting short-term consistency across repeatedly generated outputs. Experiment 2 examined linguistic variation across model updates of GPT-4o (October 2024 vs. June 2025) and GPT-4.1 (June 2025). Significant variations were observed across outputs from different model versions. GPT-4o (June 2025) generated more concise but cohesive texts, whereas GPT-4.1 (June 2025) generated outputs that are more academic, lexically sophisticated, and complex in syntax. Given the rapid evolution of LLMs and the lack of standardized methods for tracking output consistency, the current work demonstrates one of the applications of NLP-based evaluation approaches for monitoring meaningful linguistic shifts across model updates over time. Full article

Electrophoretic Displays (EPDs) are widely adopted in e-readers and portable devices due to their ultra-low power consumption and eye-friendly reflective characteristics. However, inherent hardware limitations, such as low resolution, slow response speed, and display degradation, frequently result in blurred strokes and degraded text readability. While traditional driving waveform optimizations can mitigate these issues, they are device-dependent and require extensive manual calibration. To address these challenges, this paper proposes an Improved Whale Optimization Algorithm-based Multi-scale Fusion Attention-enhanced SwinIR (IWOA-MFA-SwinIR) model for super-resolution and recognition of text images on EPDs. Structurally, the model incorporates a multi-scale fused attention (MFA) module that synergistically integrates channel, spatial, and gated attention mechanisms to precisely capture high-frequency text details while suppressing background noise within the SwinIR architecture. Furthermore, to enhance model robustness and eliminate manual tuning, an Improved Whale Optimization Algorithm (IWOA) is employed to adaptively optimize critical hyperparameters, including embedding dimension (d), attention head count (h), learning rate ($lr$), and dimensionality reduction coefficient (r). Experiments conducted on the TextZoom and EPD datasets demonstrate that the proposed model achieves state-of-the-art performance. In the ablation study, it attains a Peak Signal-to-Noise Ratio (PSNR) of 24.406, a Structural Similarity Index (SSIM) of 0.8837, and a Character Recognition Accuracy (CRA) of 89.81%. In the comparative evaluation, the proposed model consistently outperforms the second-best comparison model across three difficulty levels, yielding approximately a 1% improvement in PSNR, a 0.8% improvement in SSIM, and an 8% improvement in CRA. This confirms the proposed model’s superiority over mainstream comparative models in restoring text fidelity and improving recognition rates. Full article

Background/Objectives: To evaluate the challenges and limitations of a deep learning model for automated choroidal boundary detection in eyes with geographic atrophy (GA) using spectral-domain OCT (SD-OCT), and to assess the workflow efficiency of an AI-assisted manual verification approach. Methods: In this retrospective study, total 5723 scans (Heidelberg Spectralis) with GA were analyzed. A previously validated tool (NMI ChoroidAI) was used to segment the choroidal inner (CIB) and outer (COB) boundaries. We compared the “AI-assisted” workflow (automated segmentation followed by manual verification) against “manual segmentation only” in terms of accuracy and time consumption. Slice-wise boundary errors were graded as 0 (accurate), 1 (≤33% deviation), 2 (33–66% deviation), or 3 (>66% deviation). Outcomes included error rates and weighted F₁ score (and precision where applicable). Total time for manual-only segmentation versus AI-assisted verification was recorded. -Interreader variability was assessed between the two readers using intraclass correlation coefficient. Results: For CIB, only 5.2% of B-scans showed any deviation (strictly accurate in 94.8%), with weighted F₁ score 0.97 and precision 1.00. COB was more error-prone: 19.0% of B-scans showed deviation; however, when minor deviations were considered acceptable, COB acceptability increased to 94.2% (i.e., 5.8% remained >33% deviated). Only 13.2% of B-scans required minor manual correction. For a 97-scan volume, processing time decreased from an average of 7 h (manual only) to 45 min (AI + human verification), an approximate 90% reduction in manual effort. Inter-reader agreement was high (ICC 0.923 for CIB and 0.938 for COB). Conclusions: Although the deep learning model exhibits limitations in COB detection due to artifacts, it serves as a valuable assistive tool. Our model substantially reduces human effort, but mandatory human verification is required to correct boundary errors caused by hyper-transmission before use in clinical trials. Full article

Graphs are used in school, many occupations, and daily life, yet many people struggle to interpret them accurately. To help identify sources of difficulty in graph comprehension, we propose the Pictorial–Semantic–Task Framework. In it, we argue that accurate interpretation of graphs requires integrating pictorial variables (e.g., slope direction, graph format, data points) with semantic variables (e.g., titles, labels, scales, variable types) to determine what the graph represents. Many errors arise because readers fail to coordinate these two sources of information, often basing interpretations solely on pictorial variables. The present theoretical synthesis presents the basic analysis underlying the Pictorial–Semantic–Task Framework and an integrative review of relevant findings from graph encoding, extrapolation, and comparison tasks. The findings show that people encode and recall pictorial information far more accurately than semantic information, and often base interpretations solely on visual patterns even when semantic features call for a different conclusion. Analyses of U.S. textbooks and mass media reveal potential sources of these biased interpretations: systematic imbalances in the types of semantic information provided in textbooks and media seem likely to contribute to biases, emphasizing visual over semantic cues. By describing how perceptual and conceptual processes interact during graph comprehension, we aim to advance theories of cognitive processing in the context of graph comprehension and to derive educational implications for helping children interpret graphs more accurately. Full article

The ability to revise texts to meet the needs and expectations of the target audience requires sustained and deliberate practice. Revision becomes more complex when working on somebody’s else text and in a second language. Against this background, we conducted an exploratory and descriptive case study qualitatively shedding light on the characteristics of the processes and the products of revision. We collected data from three graduate students revising a business text in English (their second language) and from an experienced writer/editor, native English speaker, revising the same text in his first language. Using keystroke logging, screen recording, and text analysis, we observed an alternation between revision and rewriting, as well as a combination of expert features (e.g., inclusion of reader-oriented explanations) and less expert features (e.g., fewer rounds of revision) among graduate students. There were also differences between the students and the expert in the way in which they spatially organised their tasks. We interpreted these results within the context of cognitive and sociocultural models of writing, and especially the notion of agency. Full article

Major crops worldwide are affected by various diseases yearly, leading to crop losses in different regions. The primary methods for addressing crop disease losses include manual inspection and chemical control. However, traditional manual inspection methods are time-consuming, labor-intensive, and require specialized knowledge. The preemptive use of chemicals also poses a risk of soil pollution, which may cause irreversible damage. With the advancement of computer hardware, photographic technology, and artificial intelligence, crop disease recognition methods based on spectral and red–green–blue (RGB) images not only recognize diseases without damaging the crops but also offer high accuracy and speed of recognition, essentially solving the problems associated with manual inspection and chemical control. This paper summarizes the research on disease recognition methods based on spectral and RGB images, with the literature spanning from 2020 through early 2025. Unlike previous surveys, this paper reviews recent advances involving emerging paradigms such as State Space Models (e.g., Mamba) and Generative AI in the context of crop disease recognition. In addition, it introduces public datasets and commonly used evaluation metrics for crop disease identification. Finally, the paper discusses potential issues and solutions encountered during research, including the use of diffusion models for data augmentation. Hopefully, this survey will help readers understand the current methods and effectiveness of crop disease detection, inspiring the development of more effective methods to assist farmers in identifying crop diseases. Full article

Word segmentation is a fundamental component of lexical processing, and Chinese reading—lacking inter-word spacing—requires readers to identify word boundaries based on prior experience. Previous studies have shown that contextual predictability facilitates lexical identification in Chinese reading; however, its influence on word segmentation remains unclear. This study used eye-tracking to examine the relationship between contextual predictability and readers’ segmentation preferences during Chinese sentence reading. Overlapping ambiguous three-character strings (e.g., 花生长) were used as the region of interest (ROI), and a 2 (segmentation type: AB-C (e.g., 花生/长) vs. A-BC (e.g., 花/生长)) × 2 (contextual predictability: high vs. low) within-subjects design was adopted. A total of 76 native Chinese speakers completed the task. The results showed that contextual predictability had a significant effect on skipping probability: Highly predictable target character strings were skipped more often than low-predictability words. However, contextual predictability did not influence any eye-movement measure. In contrast, segmentation type produced consistent effects across all measures, with shorter reading times for AB-C than for A-BC, indicating a stable preference for two-character segmentation. More importantly, no interaction emerged between contextual predictability and segmentation type, and Bayesian model comparison further supported this conclusion. These findings suggest that Chinese reading involves a robust preference for AB-C segmentation and that contextual predictability and word segmentation operate as independent processes, with predictability exerting minimal influence on word segmentation during reading. This result supports the Chinese Reading Model (CRM). Full article

Basidiomycete mushrooms contain complex β-D-glucans which act as immunomodulator, immune stimulants and anti-cancer agents, which can be either free or bound to proteins. The present report consists of a novel and intrinsic synchronous fluorescence and phosphorescence assay method for β-D-glucans. This analytical technique was carried out by a spectrofluorometer in the range of 250 to 750 nm with a Δλ range of 5–30 nm which exhibited peaks at 492, 540 and 550 nm by using β-D-glucan from Euglena gracilis as a standard. A micro and high-throughput method based on a microplate fluorescence reader was devised with a excitation and emissions λ of 420 nm and 528 nm, respectively. This assay method revealed some advantages over the reported colorimetric methods, since it is a non-destructive assay method of β-D-glucans in samples with a linearity range of 0–14 μg/well, correlation coefficient (r2) of 0.9961, LOD of 0.973 μg/well, LOQ of 2.919 μg/well, greater sensitivity, fast, a high-throughput method and very economical. β-D-glucans of several mushrooms (i.e., Poria coccus, Auricularia auricula, Ganoderma lucidium, Pleurotus ostreatus, Cordyceps sinensis, Agaricus blazei, Polyporus umbellatus, Inonotus obliquee) were purified by using a sequence of various solvent extractions, quantified by either spectrofluorometer or fluorescence microtiter plate reader assay and compared with Congo red assay method. Three-dimensional spectra measurements were carried out on β-D-glucans from commercial sources and medicinal mushroom strains. FTIR spectroscopy was selected to investigate the structural properties of β-D-glucans in these mushroom samples. Therefore, the present assay method is simple, fast, cheap and non-destructive for β-D-glucans from medicinal mushrooms as well as from commercial sources. Full article

Background/Objectives: Parkinson’s disease (PD) is one of the most prevalent neurodegenerative disorders. Despite its multifactorial etiology, PD pathophysiology shared specific features such as cytoplasmic α-synuclein inclusions, oxidative stress, mitochondrial dysfunction, and impaired autophagy. Bromodomain and Extra-Terminal domain (BET) proteins, functioning as epigenetic readers, have recently emerged as promising therapeutic targets due to their regulatory role in redox homeostasis, neuroinflammation, and autophagy. However, their potential involvement in PD pathophysiology remains largely unexplored. Therefore, we aimed at evaluating whether BET modulation could ameliorate the parkinsonian phenotype in two cellular models. Methods: Differentiated SH-SY5Y and N1E-115 neuronal cells were exposed to rotenone toxin to mimic PD phenotype and co-treated with the small BET inhibitor JQ1. Results: BET inhibition significantly counteracted rotenone-induced cell death, neuromorphological alterations, and α-synuclein accumulation. These protective effects were accompanied by restoration of redox balance, as indicated by enhanced activation of the antioxidant system and suppression of the pro-oxidant NADPH oxidase complex. Moreover, JQ1 treatment alleviated mitochondrial dysfunction and corrected autophagy impairments triggered by rotenone. Conclusions: These data highlight a novel role for BET proteins in neurodegeneration, suggesting that their modulation may represent a promising approach to counteract PD neuropathology. Full article

For more than a century, pathology has served as a cornerstone of modern medicine, relying primarily on static microscopic assessment of tissue morphology—such as H&E staining—which remains the “gold standard” for disease diagnosis. However, this conventional paradigm provides only a snapshot of disease states and often fails to capture their dynamic evolution and complex functional mechanisms. Moreover, animal models are constrained by marked interspecies differences, creating a persistent gap in translational research. To overcome these limitations, we propose the concept of New Pathophysiology, a research framework that transcends purely morphological descriptions and aims to resolve functional dynamics in real time. This approach integrates Organ-on-a-Chip (OOC) technology, multi-omics analyses, and artificial intelligence to reconstruct the entire course of disease initiation and to enable personalized medicine. In this review, we first outline the foundations and limitations of traditional pathology and animal models. We then systematically summarize more than one hundred existing OOC disease models across multiple organs—including the kidney, liver, and brain. Finally, we elaborate on how OOC technologies are reshaping the study of key pathological processes such as inflammation, metabolic dysregulation, and fibrosis by converting them into dynamic, mechanistic disease models, and we propose future perspectives in the field. This review adopts a relatively uncommon classification strategy based on pathological mechanisms (mechanism-based), rather than organ-based categorization, allowing readers to recognize shared principles underlying different diseases. Moreover, the focus of this work is not on emphasizing iteration or replacement of existing approaches, but on preserving past achievements from a historical perspective, with an emphasis on overcoming current limitations and enabling new advances. Full article

Articles are among the most frequently encountered words during reading; however, it is not clear how deeply they are usually processed. This study examines whether native Spanish speakers use parafoveal article–noun agreement information to guide eye movements during reading. Using the gaze-contingent boundary paradigm, we manipulated the parafoveal preview of articles across two experiments. In Experiment 1, we manipulated gender agreement between the previews readers received of definite articles and the subsequent nouns (e.g., la mesa vs. el* mesa). In Experiment 2, we manipulated grammatical gender and number agreement between parafoveal article previews and the subsequent nouns jointly (e.g., los* mesa vs. una mesa). We found no evidence that parafoveal article–noun gender or number agreement affected article skipping probability, suggesting that initial parafoveal processing of articles does not extend to their grammatical properties. However, we observed increased total viewing time on the noun following mismatching previews, suggesting that, while the decision of whether to skip an article is taken largely without considering the grammatical properties of the upcoming words, readers do need more time to recover from the grammatical mismatch afterwards. We discuss the results in the context of current models of eye-movement control during reading. Full article

This article reinterprets the doctrine of iʿjāz al-Qurʾān (the inimitability of the Qurʾān) by shifting the question from what makes the Qurʾān miraculous to how it is miraculous. It argues that the Qurʾān’s primary miracle lies not merely in its content, i.e., its eloquence or correspondence with scientific truth, but in its method: the transformation of the very frameworks through which knowledge, reason, and revelation were understood. Using Muḥammad ʿĀbid al-Jābirī’s tripartite epistemology of bayān (expressive reasoning), burhān (demonstrative reasoning), and ʿirfān (reflective reasoning) together with Gadamer’s “fusion of horizons,” the article argues that the Qurʾān can be read as fusing and transcending these three systems, uniting Arabic eloquence, Greek rationalism, and Persian–gnostic spirituality into a single, holistic discourse. Through close analysis of key passages, such as Abraham’s dialectical reasoning in Sūrat al-Anbiyāʾ and the metaphysics of light in Āyat al-Nūr, the article shows how the Qurʾān integrates poetic language, rational argument, and mystical depth to create an epistemic design that addresses intellect, emotion, and spirit simultaneously. This synthesis allows the Qurʾān to be interpreted, within classical and later exegetical traditions, not only as a linguistic or theological miracle but as a paradigmatic reconfiguration of cognition: one that these traditions understood as teaching readers how to think, reflect, and awaken. Full article