Search Results (11,441)

Rapeseed is one of the most important oil crops in the world. Its yield and quality are severely restricted by biotic stress and abiotic stress. Rapeseed seeds play a crucial role in the propagation process, and the microorganisms in the seeds can be vertically passed on to the next generation, which greatly affects the quality, yield and growth of rapeseed. However, from a group perspective, there is currently a lack of systematic research on the composition of seed-associated microbiome within rapeseed seeds. This study utilized the transcriptome data of 218 rapeseed seeds that have been published, focusing on analyzing and comparing the dynamic changes and functional differences in the composition of seed-associated microbiome in rapeseed seeds under normal growth and development, biologic stress and abiotic stress conditions. Since we used public transcriptome data without surface sterilisation control, we refered to the detected microorganisms as seed-associated microbiome. The advantage of this study lies in its application of this method to a large-scale sample of rapeseed populations, which systematically revealed the response characteristics of seed-associated microbiome under different stress conditions. Interestingly, some widely distributed genera were not detected, while rare taxa were found under specific conditions, warranting further verification. Since these microorganisms originated from the seeds, their compatibility with plants and colonization ability may far exceed those of soil-derived agents. In the future, high-throughput screening of strains with excellent antagonistic or repellent effects against major diseases and pests of rapeseed can be conducted from these unique seed-associated microbiome. These strains that were confirmed by culture-based, amplicon or metagenomic approaches can then be used to develop seed coating agents or soil inoculants. Full article

Low-cost bipolar-junction-transistor (BJT) thermal pixel arrays provide robust, force-free contact sensing for tactile skins, but their slow frame rate confines contact-timing resolution to the inter-frame interval—252 ms at the 4 Hz rate of the 16 × 16 array studied here—well below the needs of contact-aware control. We propose a four-frame complementary-error-function (erfc) template, derived from one-dimensional semi-infinite heat conduction, that jointly estimates the contact amplitude, the thermal-diffusion parameter, and the sub-frame contact-onset offset (τ₁), solved by a grid-initialized semi-analytic Levenberg–Marquardt scheme (Path A) at deterministic single-pass cost. On 42 contacts from five subjects, the per-contact Cramér–Rao lower bound for τ₁ is 16.2 ms, and the empirical cross-contact dispersion is 83.5 ms; both are internal, model-derived quantities, since no synchronised external timing reference was available. A two-layer rejection pipeline separates 19/19 valid contacts from 2/2 hardware faults; transfers to four held-out subjects (23/23) without retuning; attains an overall AUC of 0.878 on a five-class synthetic disturbance library—ramp and saturating-exponential remain acknowledged failure modes; and rejects 5/6 disturbance trials in a real-airflow stress session. Larger independent cohorts and externally synchronised timing validation remain parameters for future work. Full article

Background/Objectives: The DPYD gene encodes the enzyme dihydropyrimidine dehydrogenase that metabolizes fluoropyrimidines. Genetic variants in DPYD have been associated with altered enzyme activity; therefore, accurate detection and interpretation is critical for individualized fluoropyrimidine therapy. The most common causal variant is c.1129-5923C>G (rs75017182) located in intron 10, which introduces a cryptic splice site. This variant is in high linkage disequilibrium (LD) in the HapB3 haplotype with a benign synonymous variant in exon 11, c.1236G>A (rs56038477). Since c.1129-5923C>G and c.1236G>A have been reported in LD, many commercial kits use c.1236G>A as a proxy for the function-altering intronic variant. Methods: A DPYD genotyping protocol was implemented following the quality regulations that apply to clinical laboratories (EN-ISO9001:2015 and EN-ISO15189:2022). NGS, MLPA and Sanger sequencing were used for validation purposes. Results: Over the last 5 years a total of 2007 patients have been analyzed at our department. The observed DPYD genotype frequencies aligned with those observed in European populations. Importantly, we have identified a patient harboring the c.1236G>A variant, but in the absence of the c.1129-5923C>G variant. This last result supports recently published findings suggesting that these two variants may not be in perfect LD, as previously assumed, and lead to suboptimal dosing for those patients carrying this allele. Finally, low frequency variants (c.496A>G, c.2194G>A, and c.1601G>A), not described in DPYD analysis guidelines recommendations, were found in two patients who required fluoropyrimidines dose adjustment. Conclusions: These findings highlight the limitations of relying on proxy variants for clinical decision-making, as incomplete linkage disequilibrium may lead to misclassification of patients’ metabolic capacity. Furthermore, in order to provide safer protocols for DPYD-based personalized treatment genetic panels should expand to include additional rare DPYD variants. Full article

Cardiovascular disease (CVD) is the leading cause of mortality worldwide. The healthy adult heart depends on flexible energy use, but a diseased or injured heart is associated with a loss of flexibility and metabolic remodeling. Since metabolism plays a central role in cardiac health and disease, there is a growing need to understand how metabolic reprogramming contributes to cardiac dysfunction and impaired CM maturation. Human-induced pluripotent stem-cell-derived cardiomyocytes (hiPSC-CMs) are widely used as a platform to study human cardiac development and disease mechanisms. However, current models are limited by metabolic and structural immaturity. This review provides an overview of the dynamic shifts in cardiac metabolic states from fetal development to senescence, while delineating the metabolic signatures of healthy versus disease states. These metabolic switches are orchestrated by a complex interplay of upstream signals driven by variations in substrate availability, post-translational modifications and key transcriptional regulatory networks, which ultimately regulate downstream cardiac remodeling and pathological cascades. As cardiac metabolic function is affected by a coordinated multicellular network, this review also includes the metabolic crosstalk between CMs and non-CMs, including fibroblasts, endothelial cells and immune cells. In addition, various strategies to further mature hiPSC-CMs are summarized to enhance their metabolic profiles. Investigating cardiac metabolic shifts bridges developmental biology, stem cell biology, and regenerative cardiology by revealing how energy metabolism governs cellular identity, maturation, and regenerative potential. These insights are essential for improving stem-cell-derived CMs for disease modeling, drug discovery, and heart repair. Full article

This manuscript will present an advancement of transformative research that has been conducted at Oak Ridge National Laboratory (ORNL) over a 25-year period (2000–2025) on a variety of environmental and biological matrices. These investigations derived a fundamental understanding of how elemental detection and analysis of these matrices led to the knowledge and discovery of natural processes in plants and the environment. Each project led to the initiation of a new research area which unearthed awesome and novel breakthroughs. Highlights are listed below: 1. The preliminary research at ORNL centered on the detection of aerosols utilizing Laser-induced Breakdown Spectroscopy (LIBS) technology. The Clean Air Act Amendment (CAAA) of 1990 highlighted the importance of identifying hazardous air pollutants (HAPs) due to their impact on environmental and human health, thereby underscoring the need to detect various toxic elements. Research in aerosol chemistry aimed to identify these harmful elements released by factories during periods of increased emissions in their manufacturing processes. LIBS emerged as the most effective method for real-time, in situ measurements of metal species in both gaseous and aerosol phases. 2. An understanding of the presence of total carbon in soils gives perspective on how to develop carbon sequestration strategies. The recognition that carbon sinks can evolve back to carbon sources to emit back to the atmosphere was an important consideration. Also, the concentration of carbon in soil indicates the health of land areas for growing crops successfully. 3. The direct detection of most of the elements in a wood sample in a single emission spectrum, without sample preparation, encouraged the research to use the LIBS technique for preservative treated wood coupled with use of multivariate statistical methodology. Additionally, it encouraged the researchers to try to differentiate natural woods from different parts of the country, and it was successfully demonstrated that LIBS coupled with MVA analysis could differentiate wood of different species from each other and of similar species grown in different environments based on their elemental spectra. This was a breakthrough since it revealed a systematic approach to connect elemental scarcity and abundance to either drought or typical rainfall conditions for the hardwood trees grown in specific areas. 4. Furthermore, the research progressed to reveal physiological and developmental processes contributing to biomass production such that the variation in leaf elemental composition increases our understanding of terrestrial nutrient cycles, as well as tracking the transfer of toxic elements from soils to living organisms. 5. Recently another breakthrough viz., ionomics initiated the correlation of elements to specific genes, uncovering the function that the element performed in the plant. More recently, this has been extended from plants to fungi as well as fungi growing in symbiotic relations with plants. Full article

Basic-region leucine zipper (bZIP) transcription factors are crucial for plant stress responses, but their characterization in the wild species Juglans mandshurica remains limited. Here, we identified 80 bZIP genes in the J. mandshurica genome and classified them into 13 subgroups, with notable enrichment in subgroups S, A, D, and I. All subgroup D members contain both bZIP and DELAY OF GERMINATION 1 (DOG1) domains, forming characteristic dual-module fusion proteins. Evolutionary analysis detected three orthologous gene pairs under positive selection since divergence from Juglans regia. Promoter cis-elements, especially MYB and MYC motifs, are abundant in JmbZIP genes. Protein–protein interaction networks suggest potential functional specialization and coordination among JmbZIP members. Expression profiling revealed distinct patterns across subgroups, with S, A, and D showing high activity across various physiological processes and light stress responses. qRT-PCR validated the dynamic expression of six ABA pathway marker genes, the ABRE-rich JmbZIP41 and JmbZIP42 genes, together with the highly expressed JmbZIP12 gene under salt and drought stress. Our genome-wide analysis enabled the functional screening of bZIP members across subgroups. The key genes identified in this study provide valuable genetic resources for stress-resistance breeding in forest trees, with JmABI5 (JmbZIP40) and JmbZIP42 serving as prime candidates for enhancing tree stress tolerance. Full article

The article examines how India and Pakistan have interpreted the Indus Water Treaty (IWT) in the broader context of their preference, needs, and constraints. Rather than treating the IWT as a static legal instrument or as a case of institutional resilience, the analysis conceptualizes the Treaty as a performance-based regime, where treaty stability emerges from how states perform their obligations over time rather than from institutional design alone. Adopting a qualitative process-tracing approach grounded in treaty interpretation as operationalized through state practice, this article advances three interrelated arguments: first, the durability of the IWT cannot be explained solely by institutional design, but must be understood as a “performance-based equilibrium” sustained through state practice. Second, this stability historically relied on a pattern of “compliance asymmetry,” in which India, as the upper riparian, exercised restraint well beyond minimal entitlement while Pakistan consolidated downstream dependence through infrastructural development. Third, the growing juridification of dispute resolution since the 2000s, driven by escalating infrastructural friction, has altered the political meaning of compliance, narrowed interpretive flexibility, and reshaped reciprocal expectations. The article contributes to the scholarship of international legal theory and hydro-politics, particularly by reconceptualizing treaty resilience as a function of material and political performance, rather than the formal text alone. Full article

Artificial intelligence systems that learn or reason from finite empirical data often require uncertainty representations that go beyond a single precise probability distribution. This is especially relevant when observations are scarce, incomplete or not reliable enough to support precise probabilistic assessments. In current data-driven AI tools, empirical information extracted from data must often be converted into a structured uncertainty model before it can be used for reasoning, learning or decision support. The singleton intervals induced by NPI-M and A-NPI-M provide such a representation, since they express the predictive information obtained from the observed data without introducing externally chosen cautiousness parameters. Evidence theory is useful in this context because it allows partial support to be assigned to sets of alternatives, making it suitable for representing imperfect knowledge in AI systems. This paper studies how Non-Parametric Predictive Inference for multinomial data (NPI-M) can be connected with evidence theory through reachable probability intervals. Since the exact NPI-M model does not directly define a credal set, we focus on its approximated version, A-NPI-M, which preserves the NPI-M singleton bounds and represents them through reachable probability intervals. We analyze whether the resulting credal set can be represented exactly by a belief function, showing that this is not possible in general, although exact representations may exist in particular cases. Motivated by this limitation, we construct a basic probability assignment whose belief and plausibility values reproduce the A-NPI-M singleton bounds. The resulting belief function preserves the marginal interval information of A-NPI-M while adding an evidential structure on composite events, and its associated set of compatible probability distributions is included in the A-NPI-M credal set. The construction is presented by cases, illustrated with numerical examples and compared with the belief-function representation of the Imprecise Dirichlet Model. The proposed model provides a theoretical representation layer that may support uncertainty-aware AI procedures by transforming empirical predictive information into structured imperfect knowledge before reasoning, learning or decision-support criteria are applied. Full article

The brain maintains homeostasis partially by scavenging waste products. Failure of this function is closely associated with the onset and pathogenesis of various brain diseases, such as Alzheimer’s disease, sleep disorder, and the delay of the reparative process after brain injuries. We recently demonstrated that brain pericytes (BPCs) are sources of lipocalin-type prostaglandin D synthase (L-PGDS), a waste scavenger, in the brain. Based on the above, chemical compounds which promote L-PGDS production could have potential against brain diseases, such as dementia, sleep disorders, and brain injuries. However, the specific chemical compounds that may enhance L-PGDS production in BPCs have not yet been identified. In this study, we explored 158 chemical compounds from FDA-approved drug libraries with these activities. qPCR analysis showed that mirtazapine (MTZ), a noradrenergic and specific serotonergic antidepressant, can increase L-PGDS expression in BPCs as well as in mouse- (m-BPCs) and human-derived BPCs (h-BPCs) in a dose-dependent manner. Since L-PGDS is a secretory protein, m-BPCs and h-BPCs were treated with various MTZ doses and L-PGDS levels in the culture supernatant were investigated. Western blot analysis showed that L-PGDS levels were significantly increased in a dose-dependent manner in both cell types, indicating that MTZ promoted L-PGDS secretion from m-BPCs and h-BPCs. Thus, MTZ may have the potential to be applied as drug repositioning for various brain diseases other than depression by activating L-PGDS production in BPCs, highlighting the importance of BPCs as the source to maintain brain homeostasis. Full article

Modern hands-free and wearable communication devices employ multiple microphones and loudspeakers, leading to the joint presence of acoustic echo, background noise, and desired speech signals. While acoustic echo cancellation (AEC) and beamforming are commonly combined to address this challenge, existing architectures face a trade-off between computational complexity, stability, and adaptability. In particular, adaptive beamforming approaches require repeated estimation and inversion of covariance matrices, incurring high computational cost and introducing potential sensitivity to time-varying conditions. Conversely, fixed beamformers reduce online complexity and improve stability, but their performance can degrade when the acoustic scene differs from the calibration condition. In this work, we investigate low-complexity AEC–beamforming architectures that combine fixed minimum-variance distortionless response (MVDR) beamforming with adaptive AEC. Since the ordering of these stages yields two inequivalent architectures, we evaluate two configurations: AEC followed by beamforming (AEC-BF) and beamforming followed by AEC (BF-AEC). To reduce dependence on a single head pose in wearable devices, we use an offline orientation-averaged calibration strategy in which the undesired-signal covariance matrix and, when required, the relative echo transfer functions (RETFs) are estimated from calibration measurements averaged across multiple head orientations. The proposed methods are evaluated using real-device recordings from a six-microphone wearable device. The results show a clear architectural asymmetry: the fixed BF-AEC configuration achieves the highest average echo return loss enhancement (ERLE) and perceptual evaluation of speech quality (PESQ), with substantially lower online complexity than the fully adaptive baseline, whereas the fixed AEC-BF configuration provides a higher signal-to-distortion ratio (SDR) in the evaluated experiment. Additional calibration experiments show that orientation-averaged RETF calibration provides partial generalization across the measured head orientations, but also that the RETFs are not fully orientation-invariant. Overall, the results indicate that fixed BF-AEC provides a favorable trade-off between echo suppression, stability, and online complexity under the evaluated real-recording conditions. Full article

[d=LE]Transitions of control between automated driving systems and human drivers remain safety-relevant and cognitively demanding moments in human–automation interaction. Recent studies show that transition performance depends not only on takeover timing or response speed but also on traffic complexity, driver readiness, automation limitations, trust calibration, and situational-awareness recovery. As in-vehicle interaction evolves toward conversational and agentic AI assistance, takeover support also becomes a problem of governing how natural-language AI systems communicate with the driver under uncertainty.Transitions of control between automated driving systems and human drivers remain safety-relevant and cognitively demanding moments in human-automation interaction. Recent studies suggest that transition performance should not be assessed only through takeover timing or response speed since control resumption quality also depends on traffic complexity, driver readiness, automation limitations, and situational awareness recovery. [d=LE]This paper proposes a digital-twin-mediated framework for human-aware takeover support in automated driving. In this framework, the companion AI is treated as an assumed LLM-based in-vehicle conversational or agentic assistant used as an advisory interaction component. The contribution is defined at the architectural level: human, vehicle, and context/road digital twins provide structured semantic state abstractions through a semantic state interface exposing confidence, freshness, provenance, and consistency metadata, while a trustworthy companion AI (TCAI) layer grounds, constrains, validates, and governs companion AI output proposals before HMI delivery.This paper motivates and defines a trustworthy companion AI (TCAI) layer for human-aware transition support in automated driving. The TCAI is conceived as a bounded, supervised, and explainable advisory agent that supports the driver without entering the safety-critical vehicle-control loop. It reasons over structured semantic state abstractions derived from a human digital twin, a vehicle digital twin, and a context/road digital twin, exposing driver readiness, automation capability, and contextual urgency in a form that supports traceable, uncertainty-aware, and degradation-aware assistance. [d=LE]Building on the research on driver-state monitoring, adaptive HMI, trust calibration, explainability, conversational assistance, and human assistance systems (HASs), the framework coordinates advisory interaction across vigilance support, contextual explanation, trust-calibrating communication, and directive handover guidance. The TCAI layer combines bounded reasoning, human-factor-derived guardrails, state-consistency management, dynamic explanation-depth control, trust-dynamics modeling, graded watchdog veto handling, mandatory access-control assumptions, and deterministic fallback. Safety-critical vehicle-control and minimum risk condition (MRC) functions remain assigned to the deterministic vehicle-control stack, while the authorized output path of the TCAI layer is validated HMI delivery.Building on the research on driver-state monitoring, adaptive HMI, trust calibration, explainability, and conversational assistance, we propose a conceptual architecture in which the TCAI coordinates multimodal assistance across different interaction conditions, including vigilance support, contextual explanation, trust-calibrating communication, and directive handover guidance. The companion does not actuate the vehicle; its outputs are constrained by runtime governance, policy enforcement, and deterministic fallback mechanisms. [d=LE]The paper concludes with a validation agenda and technical roadmap covering planned transitions, urgent handovers, degraded or adversarial conditions, temporal fusion of driver-state evidence, phase-sensitive HMI policies, trust-calibration trajectories, driver veto and partial-disabling mechanisms, and staged simulator-to-vehicle evaluation. Although motivated by SAE Level 3 automation, the framework may also inform fallback-related Level 4 scenarios in which human and automated agency must be managed under uncertainty.The paper concludes with a research roadmap for validating the proposed architecture under planned transitions, urgent handovers, and degraded or adversarial conditions. Although motivated by SAE Level 3 automation, the approach may also inform fallback-related Level 4 scenarios. Full article

Cremastra appendiculata is a medicinally important orchid whose seed germination depends on fungal symbionts. Here, we present the first telomere-to-telomere (T2T) genome assembly of the orchid mycorrhizal fungus Coprinellus disseminatus, comprising 15 gapless chromosomes (54.41 Mb) with 98.80% BUSCO completeness. Symbiotic germination assays demonstrated that C. disseminatus significantly outperformed its congeners C. domesticus and C. radians in protocorm biomass. Comparative genomic analyses revealed highly conserved carbohydrate-active enzyme (CAZyme) repertoires among the three species, ruling out CAZyme divergence as the primary driver of differential symbiotic performance. CAFE analysis showed that since its divergence approximately 117.8 million years ago, C. disseminatus underwent substantial gene family expansions enriched in proteasome, endocytosis, adherens junction, and tight junction pathways, suggesting that lineage-specific expansion of these functional modules may have contributed to its superior symbiotic capacity for orchid seed germination. These findings require further experimental validation through transcriptomic and functional genomic approaches. Full article

The growing prevalence of chronic degenerative diseases has increased interest in nutritional strategies based on natural bioactive compounds such as polyphenols. This study aimed to develop a polyphenol-fortified tomato juice using extracts from unripe green tomatoes and to evaluate its physicochemical, antioxidant, sensory, and storage properties. Polyphenolic extracts obtained from tomato by-products were characterized using spectrophotometric and HPLC analyses and incorporated into tomato juice, which was then pasteurized and stored for six months. Total polyphenol content increased from 40.97 to 82.45 mg GAE/100 g, decreasing to 71.44 mg after storage; HPLC confirmed higher levels of key phenolic compounds in fortified juice. DPPH antioxidant activity increased in fortified juice compared to control, since pasteurization had limited effects but decreased after storage, with a moderate reduction in bioactivity. Colorimetric and sensory analyses showed changes in color, aroma, and sweetness after storage, potentially affecting consumer acceptance, although overall composition remained largely stable. Overall, results demonstrate the feasibility of producing a polyphenol-enriched tomato juice from agro-industrial by-products with improved antioxidant properties and acceptable technological stability. These findings support the valorization of tomato processing waste and suggest potential applications in functional food development, human health promotion, and the sustainability of agri-food systems’ overall approach. Full article

Across all kingdoms of life, ribosomes are indispensable molecular machines that translate genetic information into the proteome of living cells. The fundamental catalytic centers of the ribosome, constructed primarily from ribosomal RNA (rRNA), exhibit remarkable conservation between the major domains of life. The ribosome’s A-site deciphers the mRNA’s triplet code, while the P-site synthesizes the growing protein chain and the E-site provides exit for deacylated tRNA; a distinct tunnel facilitates nascent polypeptide export. While the conservation of ribosomal proteins is less pronounced between bacteria and eukaryotes, striking homology exists from simple eukaryotes to humans. Ribosomal proteins were traditionally viewed mainly as scaffolding agents, steering rRNA folding during ribosome biogenesis and maintaining structural stability during translation. However, since the early 2000s, advances in structural and functional ribosome analysis have ushered in a more nuanced paradigm: ribosomes are no longer considered uniform machines. Instead, an array of rRNA and ribosomal protein modifications generates a spectrum of ribosome populations capable of specialized translation. RiboScreen^TM technology leverages this regulatory potential of individual ribosomal proteins, enabling deliberate modulation of target protein output and representing a promising tool for correcting dysregulated protein expression involved in rare and common diseases. This review will first introduce relevant aspects of ribosome biology and then showcase the tools of this new technology. Finally, we report examples for the delivery of small molecules to target ribosomal proteins for tailored restoration of protein production levels in rare and prevalent diseases. Full article

A novel modified generalized Riccati equation mapping neural network-based approach is the basic theme of this study by exploring the nonlinear dynamical characteristics of the the strain wave model’s soliton solutions, which govern wave propagation in micro structured solids. Strain waves are particularly intriguing, since they preserve their form and speed throughout transmission. The nonlinear dynamical behaviors of strain waves may be modeled by partial differential equations in micro structured materials. In the realm of micro structured solids, there exists a class of phenomena that are referred to as micro strain waves. These waves arise in solids possessing intricate internal architectures, including periodic lattices, precisely engineered metamaterials Understanding these waves is key to designing more complex materials and new acoustic technologies. The activation function and the weight function of the neural network are assigned to each input layer, hidden layer and output layer and the neural network itself is a multi-layer computational network. Using the structure of the neural network, every neuron in the first hidden layer is given solutions to the Riccati equation, and the new highly expressive trial functions are generated in a systematic way. In this way, a large variety of exact soliton solutions are obtained, such as bright, dark, kink, and combined solitons as well as periodic and hyperbolic wave profiles. The influence of the essential physical and mathematical parameters is explored systematically using three-dimensional, two-dimensional and contour visualizations, which illustrate how parameter variations lead to changes in the amplitude, shape and stability of the wave structures. The solutions presented reveal the dynamic properties of micro strain solitons which leads to new avenues of investigation in the study of related nonlinear phenomena in micro structured solids. In a broader context, our results highlight the great potential of analytical techniques using neural networks as a powerful and versatile toolset to study complex nonlinear wave models within the applied sciences from acoustics to photonics to smart materials engineering. Full article