Search Results (174)

Low-temperature-induced fertility restoration in thermo-sensitive genic male sterile (TGMS) lines severely impairs hybrid seed purity, which is a major bottleneck for two-line hybrid rice production. Most commercial TGMS lines rely on the single tms5 locus, leading to high climatic vulnerability. In this study, we developed a dual-locus strategy by target genome editing of TMS5 and MS1 in indica rice GH89. Adenine base editing at the MS1 locus exhibited a high editing efficiency of 93.5%. Transgene-free homozygous single mutants (GH89-tms5 and GH89-MS1) and double mutant (GH89-tms5 + MS1) were generated for phenotypic analysis. The double mutant GH89-tms5 + MS1 remained completely sterile for 5 and 10 days under controlled low temperature (23.5 °C), with only minimal fertility restoration after 15 days. In the field, it maintained complete sterility for 84 consecutive days and was fully insensitive to short-term low temperature fluctuations, outperforming single mutants and commercial control Y58S. Moreover, the double mutant retained most key yield-related agronomic traits of the wild type with only minor variations. This dual mutation forms a “double-lock” fertility regulatory system, significantly increasing the low-temperature duration threshold for fertility restoration. The GH89-tms5 + MS1 line exhibits promising potential for future rice breeding applications. Full article

This study examines the phenomenon of pseudoconfident knowledge in the context of the everyday use of generative artificial intelligence. By pseudoconfident knowledge, we mean a response that is substantively plausible, rhetorically coherent, and outwardly persuasive but is treated and understood as knowledge before its actual reliability has been established. Of course, we do not use the term “pseudoconfident knowledge” to denote knowledge in the strict epistemological sense. Rather, it denotes a special form of AI-generated content that acquires the status of knowledge in the user’s perception before its reliability, source-based justification, or factual correctness have been established. The problem here is not that such an answer is already knowledge but that it is prematurely accepted as knowledge because of its coherence, completeness, and rhetorical confidence. The aim of the study is to identify the epistemic gap between the everyday operational integration of artificial intelligence and the user’s critical ability to distinguish between persuasiveness and justification. The theoretical framework combines approaches to AI literacy, epistemic vigilance, and contemporary forms of digital mediation in the circulation of knowledge. The empirical basis of the study is an online survey of AI users. The analysis was conducted using descriptive statistics, contingency tables, and methods for testing associations between categorical variables. The results show that the key differentiating factor is not the frequency of AI use, but the strategy used in handling its responses. More epistemically robust positions are associated with practices of comparison, editing, and verification, whereas uncritical acceptance of the answer is associated with greater vulnerability to pseudoconfident knowledge. We conclude that the spread of generative artificial intelligence is producing a new socioepistemic problem that calls for a shift in emphasis from simple instrumental literacy toward a culture of verification, doubt, and epistemic responsibility. Full article

This research proposes a conditional diffusion-based workflow for early-stage floor plan design in university research buildings, addressing complex functional organization, strict boundary constraints, and quantitative area control. The method performs denoising directly in two-dimensional grid space and coordinates building outlines and functional area proportions through dual-condition injection using boundary masks and functional area matrices. A two-stage generation mechanism first constructs horizontal circulation and then generates the complete layout, while a statistic-network-guided explicit constraint improves global area consistency. Based on 600 standard-floor samples and an independent test set of 10 real projects, the method is evaluated through model comparison, ablation, and double-blind experiments. The results show that the proposed model achieves the best overall performance, with an FID of 50.3, a building boundary IoU of 99.9%, and horizontal circulation connectivity of 89.8%. The ablation results confirm that the two-stage mechanism and explicit statistical constraint substantially improve generation success and reduce area error. The expert evaluation indicates that AI-generated floor plans approach real cases in functional spatial form and design inspiration, although spatial organization rationality still requires improvement. The generated layouts can be converted into layered DXF files, supporting subsequent editing and human–AI collaborative design. Full article

The lamin proteins are classified into A- and B-types, and together with their associated proteins, they form the nuclear lamina, which governs diverse nuclear structures and functions, including nuclear mechanics, chromatin organization, and gene regulation. Mutations of these proteins give rise to a strikingly diverse group of tissue-specific disorders, the laminopathies, including muscular dystrophies, cardiomyopathies, lipodystrophies, neuropathies, and premature aging syndromes, despite their broad expression. Unraveling the basis of this tissue selectivity has revealed that lamins function not merely as structural elements but as active regulators. While the A-type lamins modulate nuclear stiffness, transcription, and genome integrity, the B-type lamins ensure mechanical resilience and heterochromatin tethering. Pathogenic mutations of these proteins disrupt their functions through convergent mechanisms that manifest according to tissue-specific contexts, leading to impaired nuclear mechanics, aberrant gene regulation, defective DNA repair, and cellular senescence. Advances in patient-derived cellular models and animal systems have illuminated these vulnerabilities and catalyzed therapeutic progress, ranging from farnesyltransferase inhibitors to emerging genome-editing strategies. Collectively, studies of lamin protein function reveal how the nucleus maintains its structures and functions, while studies of laminopathies demonstrate how nuclear dysfunction drives systemic disease and points toward mechanism-based therapies. Full article

Plants are constantly exposed to a wide range of abiotic stresses that have significant negative impacts on growth and yield. Plant acclimation to these stresses is governed by integrated classical phytohormone and plant peptide hormone signalling networks that control the ability of a plant to survive and adapt to extreme environments. Classical phytohormones, including abscisic acid, auxins, gibberellins, cytokinins, jasmonates, salicylic acid, brassinosteroids, and the recently recognised phytomelatonin, act in concert with peptide-based plant hormones, among which C-terminally encoded peptides (CEPs) play prominent roles in coordinating stress perception, signal transduction, and adaptive responses throughout the plant. These integrated networks control stomatal behaviour, photosynthesis, osmolyte and antioxidant levels, root architecture, and energy metabolism, thereby helping plants maintain homeostasis and optimise survival while sustaining minimal growth under unfavourable conditions. Under stressful conditions, these networks do not operate in isolation but form highly dynamic, context-dependent regulatory circuits in which each physiological process is simultaneously regulated by multiple hormones acting through convergent and overlapping signalling pathways. Phytomelatonin has emerged as a particularly important integrative node within these networks, functioning both as a potent direct antioxidant through sequential ROS-scavenging catabolite cascades and as a bidirectional regulator of classical phytohormone signalling under diverse abiotic stresses. New technologies in the fields of transcriptomics, proteomics, phosphoproteomics, metabolomics, and systems biology have provided new information on the dynamic relationships between classical phytohormones and plant peptide hormones, revealing candidate regulatory nodes and transcription factor networks that mediate stress adaptation at molecular, biochemical, and physiological levels. However, it is important to distinguish between correlative associations identified through omics profiling and causal regulatory relationships validated through rigorous genetic and biochemical experimentation, as most omics-derived candidates remain to be functionally established. Empirical studies demonstrate how these networks can be used to improve crops by increasing stress tolerance through modulating classical phytohormone and plant peptide hormone signalling, including through exogenous phytomelatonin application, CRISPR-mediated hormone pathway editing, and CEP pathway manipulation, to produce resilient cultivars without reducing yields. Although these advances represent significant progress, challenges remain, including the inherent complexity and redundancy of the networks, context-dependence and severity-dependence of hormonal responses, the persistence of a significant translational gap between laboratory findings and field application, and incomplete mechanistic understanding of peptide hormone roles under combined stress conditions. Addressing these challenges will require integrative multi-omics approaches, higher-order computational modelling, and rigorous field-based functional validation alongside emerging tools such as synthetic biology and precision breeding. Full article

As sessile organisms, plants must dynamically allocate resources between growth and stress resilience. This review focuses on Ethylene Response Factor (ERF) transcription factors as central regulators of this fundamental balance. We evaluate the molecular basis of ERF function, highlighting their modular structure, dynamic post-translational regulation, and ability to form context-specific protein complexes that integrate diverse signals. While ERF family members show functional redundancy, certain ERF subgroups, such as the ERF-VIIs, exhibit clearer evidence of dual roles in coordinating both developmental programs and adaptive responses to stress. We further elucidate the mechanisms underlying ERF-mediated trade-offs, explaining how these factors direct spatial resource allocation and enable temporal switching between growth and defense states. Finally, we explore how emerging technologies, such as CRISPR-based genome editing and various synthetic biology tools, can harness ERF regulatory networks. These approaches offer promising strategies for engineering crops with precisely tuned adaptive capacity, supporting sustainable agriculture even in changing climate conditions. This synthesis highlights specific ERF subgroups as pivotal integrators and future targets for crop improvement. Full article

Real-time PCR (qPCR) is today’s definitive quantitative technology in molecular biology and diagnostics. Until 30 years ago, PCR product analyses were generally performed after amplification using gel-based methods. Quantification typically relied on visual inspection or densitometry of end-point products and was therefore relatively unreliable and poorly suited to high-throughput automation. To celebrate real-time PCR’s 30-year anniversary of commercial availability, Professor Stephen Bustin, Guest Editor for the special edition, “Advancing Molecular Science Through Reproducible qPCR: MIQE Guidelines and Beyond,” asked Russell Higuchi to give a historical account on how his idea of real-time PCR was conceived and brought to fruition. Dr. Higuchi then asked his collaborator, Lincoln McBride, who drove the development of the ABI 7700—the high-throughput real-time PCR instrument that gave researchers access to this technology—to co-author this dual memoir. This story is told from the perspectives of the two scientists most directly responsible for making real-time PCR practical and widely accessible. Taking turns, Russell Higuchi describes the conceptual and experimental steps at Cetus and then Roche that led from homogeneous PCR detection to continuous fluorescence monitoring, whilst Lincoln McBride details ABI’s parallel efforts to commercialize Russ’s invention. Together, they trace how experimental insight, engineering constraints, product development, and commercial decision-making shaped the Applied Biosystems 7700 Sequence Detection System and established real-time PCR as a practical and reliable quantitative technology. Their team’s efforts persevered through technological uncertainty and within a complex corporate collaboration. They share key historical documents in their original form. Their accounts show how the 7700 system emerged as the convergence of chemistry, optics, software, and product development. The eventual global reliance on real-time PCR during the COVID-19 pandemic demonstrated, at unprecedented scale, the profound and enduring impact of these early technical and organizational choices. Full article

Background/Objectives: Attention-Deficit/Hyperactivity Disorder (ADHD) shows substantial cognitive heterogeneity, complicating individualized clinical formulation. This study examined whether Cognitive Disengagement Syndrome (CDS), anxiety, and ADHD symptom severity are associated with memory functions and visuospatial skills in children with ADHD. Methods: The sample included 120 children aged 6–12 years with ADHD (ADHD + CDS: n = 40; ADHD-only: n = 80). Memory was assessed with the Oktem Verbal Memory Processes Test (OVMPT) and Wechsler Memory Scale–Visual Reproduction (WMS–VR), and visuospatial skills with WISC-IV Block Design and Judgment of Line Orientation (JLO). ADHD symptoms were rated using combined parent–teacher Turgay-Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition-Based Disruptive Behavior Disorders Scale (T-DSM-IV-S) scores; CDS symptoms with the Barkley Child Attention Scale; and anxiety with the SCARED-Child Form. Group comparisons, correlation analyses, and multivariable linear regression models were conducted. Results: The ADHD + CDS group performed worse on WISC-IV Block Design than the ADHD-only group (p = 0.005). In the ADHD + CDS group, inattention severity showed a strong negative association with WMS–VR short-term memory (r = −0.560, p < 0.001). In the ADHD-only group, inattention severity was negatively associated with OVMPT Spontaneous Recall (ρ = −0.319, p = 0.004) and JLO total score (ρ = −0.348, p = 0.002). Anxiety severity in the ADHD-only group was positively associated with OVMPT Total Learning (ρ = 0.350, p = 0.001), Highest Learning (ρ = 0.370, p = 0.001), and WMS–VR short-term memory (ρ = 0.304, p = 0.006). In regression analyses, the presence of CDS independently and negatively predicted WMS–VR short-term memory (β = −0.187, p = 0.018) and Block Design performances (β = −0.226, p = 0.016). Inattention symptom severity was also independently and negatively associated with Block Design performance (β = −0.243, p = 0.013). Conclusions: CDS status and symptom dimensions contribute to cognitive variability in pediatric ADHD, with CDS showing independent associations with timed visuospatial construction and short-term visual memory. Inattention severity emerged as a robust dimensional predictor of cognitive inefficiency across domains, supporting the clinical utility of symptom-based cognitive profiling in ADHD diagnostic evaluations. In addition, mild anxiety symptoms demonstrated meaningful associations with some learning and memory performances within the ADHD-only group, indicating that affective factors may modulate cognitive outcomes in ADHD. Taken together, these findings support considering CDS status and symptom dimensions jointly when characterizing cognitive variability in ADHD. Full article

A large number of architectural drawings have historically existed in paper form or as non-editable raster images, which makes them difficult to directly support information reuse and digital management, while manual CAD reconstruction is time-consuming and inefficient. This paper proposes a hybrid deep learning and rule-based method for architectural drawing vectorization and CAD reconstruction, which automatically converts scanned raster images into editable CAD vector files while preserving geometric structure and scale consistency. The proposed method consists of four modules: axis grid and dimension detection, text recognition and scale recovery, architectural line topology reconstruction, and CAD geometric rectification and reconstruction. The method utilizes object detection and OCR technologies to extract key semantic information from the drawings. By extracting semantic information, the method constructs a line topology structure and applies architectural drawing constraints to parameterize and normalize geometric results, thereby achieving the recognition and vectorization of raster drawings. Experimental results and engineering case studies demonstrate that the proposed method can effectively extract typical architectural elements, and generate directly editable CAD vector drawings. The method achieves favorable geometric accuracy and topological consistency in architectural drawing digitization and automatic CAD reconstruction tasks, providing a technical solution for the automatic vectorization of existing architectural drawings. Full article

A/T(U) and G/C nucleobase pair formation in DNA and RNA is crucial to numerous fundamental biological processes, including replication, transcription, and translation. The specificity of A/T(U) and G/C base pairing is used for the recognition of complementary sequences in medical and biotechnological applications, such as PCR, nucleic acid drugs, and CRISPR–Cas9-based gene editing. It is essential to understand and predict fidelity of biological reactions, avoiding off-target binding, in order to improve the accuracy and efficacy of applications. In particular, recognition mechanisms of complementary bases or whole sequences must be understood in detail. Despite the prevailing view that Watson–Crick hydrogen bonding is a primary mechanism for complementary base recognition, several experiments have shown that DNA polymerase does not require hydrogen bonding to select complementary bases. Other factors, such as the shape and geometric fitting of the bases and the base stacking, also appear to be crucially involved in the selection. E.g., artificial bases lacking the ability to form hydrogen bonds can still be recognized by DNA polymerase solely based on base-pair geometry. However, hydrogen bonding also contributes importantly to recognition. The accuracy of selecting a complementary nucleobase or sequence varies depending on reactions, suggesting the co-existence of multiple selection mechanisms. This review provides an overview of biological processes and applications involving base pairing and discusses the molecular mechanism underlying complementary base recognition. Full article

Approaches to delivering gene editing tools in the form of ribonucleoproteins may provide a safety advantage over the delivery of nucleic acids encoding ribonucleoproteins. Virus-based vectors are widely used as a delivery platform. However, the persistence of viral exogenous nucleic acids can cause increased genotoxicity. Virus-like particles (VLPs) do not contain an expression cassette and can act as a platform for the delivery of ready-made ribonucleoprotein complexes. The absence of nucleic acids in VLPs eliminates the risk of insertional mutagenesis compared to widely used lentiviruses or adeno-associated viruses. Therefore, we used VLPs to deliver the ribonucleoprotein complex MmCas12m–TadDE to disrupt the HIV-1 gag gene start codon. We detected VLP morphogenesis using electron microscopy. We confirmed the incorporation of MmCas12m–TadDE into VLPs. We achieved an editing efficiency of about 9% in some cases with minimal off-target effects, which confirms the prospect of using VLPs as a platform for delivering genomic editing tools. Full article

Glioblastoma multiforme (GBM) remains the most aggressive and treatment-refractory form of primary brain tumor in adults, characterized by rapid proliferation, intratumoral heterogeneity and resistance to current therapies. Despite therapeutic advancements in surgical resection, radiotherapy and chemotherapy, clinical outcomes remain poor, underscoring the need for innovative molecular strategies. This review examines the therapeutic potential of CRISPR/Cas9 genome-editing technologies in GBM, highlighting their ability to model, dissect and potentially correct the genetic alterations that drive GBM tumorigenesis. Key molecular targets, such as EGFR, PTEN, TP53, NF1 and PIK3CA, are discussed within the context of GBM’s mutational and signaling landscape. We further outline emerging CRISPR applications in preclinical models, the current status of CRISPR-based clinical trials and the major barriers hindering translation, including off-target effects, immunogenicity and the challenge of delivering gene-editing systems across the blood–brain barrier. Particular emphasis is placed on delivery technologies, viral and non-viral vectors, including lipid nanoparticles, polymeric systems, inorganic nanocarriers and DNA nanostructures, which are rapidly evolving to improve precision, safety and CNS penetrance. Collectively, this review highlights CRISPR/Cas9 as a powerful tool whose integration with molecular neuro-oncology and precision medicine may ultimately shift GBM treatment toward more personalized and durable therapeutic interventions. Full article

With ongoing growth in the implementation of CCTV networks, miniature sensors, and IoT devices, the quality of captured images in terms of authenticity has become a major security issue. Through advanced editing tools and generative models, the capability now exists to perform highly advanced forgeries that fail both human perception and traditional algorithms, and especially in terms of sensor-generated content. State-of-the-art algorithms typically use a single-cue characteristic in their models to stabilize performance, including local noise statistics or structural disruption patterns, making them susceptible to varied forms of manipulation. As a solution to this issue, we have developed MultiFusion, a new forgery detection framework which combines complementary forensic cues in images: SRM-based noise residuals, hierarchical texture features based on EfficientNet-B0, and global structural relationships from a vision transformer. A special DnCNN denoising preprocessing layer represses sensor noise and maintains fine traces of tampering. To achieve better interpretability, we combine Grad-cam images of the convolutional flow and transformer attention maps to create on-unit interpretable heatmaps, the areas of which identify regions of manipulation. Experimental verification on the CASIA 2.0 standard shows high detection accuracy (96.69) and good generalization. Via normalized denoising, multimodal feature fusion, and explainable AI, our framework takes CCTV, sensor forensics, and IoT image authentication to the next level. Full article

Plant viruses cause substantial yield and quality losses worldwide, and their rapid evolution can erode deployed host resistance. This review synthesizes current knowledge of antiviral resistance and tolerance mechanisms, using barley yellow dwarf virus (BYDV) in cereals as an illustrative case study. We first summarize key layers of plant antiviral immunity, including pre-formed physical and chemical barriers, dominant and recessive resistance genes, RNA silencing, hormone-regulated defense signaling, and degradation pathways such as the ubiquitin–proteasome system and selective autophagy. We then discuss how these mechanisms are exploited in breeding and biotechnology, covering conventional introgression, marker-assisted selection, QTL mapping and pyramiding, induced variation (mutation breeding and TILLING/ecoTILLING), transgenic strategies (pathogen-derived resistance and plantibodies), RNA interference-based approaches, and CRISPR-enabled editing of susceptibility factors. Finally, we highlight emerging nano-enabled tools and propose integrated strategies that combine genetic resistance with surveillance and vector management to improve durability under climate change and ongoing viral diversification. Full article

Despite being the most popular sutra tableau in Dunhuang, the utter lack of any comprehensive, or chronological academic analysis even in Chinese calls for a thorough research on the Medicine Buddha Sutra iconography at Dunhuang. This paper will explore the Medicine Buddha both in the literary form, the sutras, and the visual form, the sutra tableaux, when they first appeared in China during the Sui Dynasty. First, the relevant sections of the four Medicine Buddha Sutra translated in Chinese will be examined in detail. Then, the earliest four pictorial representations, namely Caves 417, 433, 436, and 394 at Dunhuang, will be scrutinized to establish a firm foundation of this said sutra tableau for later periods. By comparing the deities, and other special attributes presented in these images with what were recorded in the sutras, this paper reveals how the anonymous monastics and artists “re-presented” the Medicine Buddha from literary form to pictorial form, which embodies the intermediallity during the Sui Dynasty in Dunhuang. After analyzing how the textual elements such as the Medicine Buddha, attendant Bodhisattvas, Twelve Demigods, Four Heavenly Kings, and the magical life-prolonging instruments were depicted in the paintings, intermediality between the texts and imagery is brought to light. Two most decisive details, the small sizes of the cartouches for the inscriptions of the Twelve Demigods, and the number of Medicine statues that should be present at the ritual, clearly show the Medicine Buddha Sutra imagery painted during the Sui Dynasty in Dunhuang is based on the earliest Chinese edition, Sutra on the Initiation to Remove Unwholesome Deeds and Attain Salvation from Birth and Death Taught by the Buddha, translated by Śrīmitra. Full article