Browse Articles

The distinctive innovation patterns and network embeddedness of scientific prizewinners

Science prizes purportedly reward innovation and explorations of new phenomena. Yet in practice, prizes may inadvertently divert resources from similarly impactful but less celebrated scholars. Despite this paradox, and even as prizes proliferate, knowledge of how prizewinning relates to innovation is nascent. Analyzing 2,460 worldwide prizes, we compared the innovativeness of over 23,000 prizewinners and matched nonprizewinners whose performance records were statistically equivalent up to the prize year. First, we find that prizewinners are more innovative. Their research is more likely to combine existing ideas in new ways, integrate a topic’s historical and contemporary thinking, and incorporate interdisciplinary perspectives. Second, although prizewinners and matched nonprizewinners have statistically equivalent impact and productivity records up to the prize year, at about five years before the prize, prizewinners’ papers become more innovative than their matched peers. This difference widens each year, peaks during the prize year, and then persists for the remainder of their careers. Third, network embeddedness predicts unusual innovativeness. Compared to nonprizewinners, prizewinners’ collaborations are shorter in duration, encompass wider exposure to unfamiliar topics, and involve coauthors whose networks minimally overlap with each other. The findings’ implications for innovation in science and the efficacy of reward systems and innovation in science are discussed.

Job satisfaction as a mediator between organizational factors, work environment, and burnout among Jordanian midwives

Tyrosine phosphorylation coupling of one-carbon metabolism and virulence in an endogenous pathogen

Endogenous pathogens can constrain virulence to ensure survival in the host. Pathogenic state can be controlled by metabolic responses to the prevailing microenvironment; however, the coupling and effector mechanisms are not well understood. Flux through the one-carbon metabolism (OCM) pathway can modulate virulence of the oral pathobiont Porphyromonas gingivalis , and here we show that this is controlled by tyrosine phosphorylation-dependent differential partitioning of gingipain proteases. The OCM essential precursor pABA inhibits the low molecular weight tyrosine phosphatase Ltp1, and consequently relieves inhibition of its cognate kinase, Ptk1. We found that in the absence of pABA, reduced Ptk1 kinase activity blocks extracellular release of gingipains. Surface retention of gingipains confers resistance to neutrophil mobilization and killing, and virulence in animal models of disease is elevated. Reciprocally, Ptk1 and gingipains are required for maximal flux through OCM, and Ptk1 can phosphorylate the OCM pathway enzymes GlyA and GcvT. Further, ALP, an alkaline phosphatase involved in synthesis of DHPPP, which combines with pABA to make DHP, is phosphorylated and activated by Ptk1. We propose, therefore, that although the primary function of Ptk1 is to maintain OCM balance, it mechanistically couples metabolism with tunable pathogenic potential through directing the location of proteolytic virulence factors.

Data-driven scenario analysis supports the revival of historic silvoarable systems for carbon smart rural landscapes

Abstract Agroforestry has long been recognised as a nature-based solution for climate mitigation, yet its adoption in Europe has drastically declined due to the socio-economic transformations and land use intensification since the onset of the Great Acceleration (ca. mid-twentieth century). This study reconstructs the historical role of agroforestry in Northern Italy by drawing on century-long land use records (1929–2024) and historical sources, which were crucial for identifying and modelling the carbon stock of traditional silvoarable systems. Through the integration of Monte Carlo simulations and scenario-based modelling, we estimate that historic silvoarable systems stored an average of 75.4 t C ha−1, with a potential range of 50.4–101.6 t C ha−1. The widespread abandonment of agroforestry practices led to a 97% reduction in their extent, accompanied by a corresponding expansion of monocultures. Future management scenarios suggest that restoring silvoarable systems could enhance regional carbon sequestration by up to 12%, a gain comparable to afforestation strategies requiring the conversion of 25% of existing farmland. Our findings underscore the global value of traditional ecological knowledge and historical land use strategies in informing carbon-smart agricultural transitions and shaping policies for resilient, multifunctional landscapes.

Trends in the double burden of malnutrition among Indonesian adults, 2007 to 2023

Investigation on tribological and mechanical behaviour of GFRP composites with varying weight percentages of nano-graphite powders

Hierarchical woven fibrillar structures in developing single gyroids in butterflies

Nature offers a remarkable diversity of nanomaterials that have extraordinary functional and structural properties. Intrinsic to nature is the impressive ability to form complex ordered nanomaterials via self-organization. One particularly intriguing nanostructure is the gyroid, a network-like structure exhibiting high symmetry and complex topology. Although its existence in cells and tissues across many biological kingdoms is well documented, how and why it forms remains elusive and uncovering these formation mechanisms will undoubtedly inform bioinspired designs. A beautiful example is the smooth single gyroid that is found in the wing scales of several butterflies, where it behaves as a photonic crystal generating a vibrant green color. Here, we report that the gyroid structures of the Emerald-patched Cattleheart, Parides sesostris , develop as woven fibrillar structures, in contrast to the commonly held assumption that they form as smooth constructs. Ultramicroscopy of pupal tissue reveals that the gyroid geometry consists of helical weavings of fibers, akin to hyperbolic line patterns decorating the gyroid. Interestingly, despite their fibrillar nature, electron diffraction reveals the absence of crystalline order within this material. Similar fibrillar structures are also observed in the mature wing scales of P. sesostris specimens with surgically altered pupal development, leading to a blue coloration. Our findings not only introduce a variation of the gyroid in biology but also have significant implications for our understanding of its formation in nature.

The relationship between curve of Spee modification and changes in temporomandibular joint parameters: a CBCT-based study

Genetic variation of barley genotypes using morphological traits, amylose content, and molecular markers

Abstract For any plant breeding endeavor to be effective, a variety of genetic resources must be available and accessible. Using simple sequence repeats (SSRs) markers, amylose content, and agro-morphological characterization, the genetic diversity of 50 barley genotypes was evaluated. Across 50 genotypes, analysis of variance revealed highly significant variation (p < 0.01) in each trait. The genotypes G20, G7, G18, G28, G41, G45, G50, G13, G39, and G47 displayed the highest yields. Positive correlations were found between the number of tillers per plant, plant height, spike length, number of grains per spike, 1000-grain weight, and grain yield. Cluster analysis sorted genotypes into five different groups. Cluster I had a minimum of four genotypes, and a maximum of forty-four genotypes were found in cluster V. Ninety-nine percent of the overall diversity among genotypes was accounted for by the first five main principal components (PCs). The contents of amylose and amylopectin were 13–29% and 71–87%, respectively. The genotypes G4, G5, G8, G12, G19, G34, G6, G37, and G41 exhibited the greatest amylose content. These genotypes might be chosen to increase quality and achieve the desired levels of amylose and amylopectin. Significant genetic variety was revealed among 50 barley genotypes by molecular analysis using 20 SSR markers. They had an average of 2.95 alleles per locus, ranging from 2 to 4 alleles. With an average of 0.52 per locus, the polymorphism information content (PIC) value varied from 0.31 to 0.67. A similarity matrix was created using the Unweighted Pair Group Method with Arithmetic Mean methodology, which divided the tested genotypes into five major groups for the determination of the genetic relatedness of barley genotypes. The SSR-based analysis clustered the genotypes into four different groups. The different grouping based on agro-morphological and SSR markers suggests a relationship between allelic profiling and expression profiling, or between genomic DNA and agro-morphological traits. It could be very helpful for future barley research. Barley genotype identification may benefit from the identified DNA markers. Similar to this, the high level of genetic diversity observed in the genotypes may be helpful in developing strategies for the management and conservation of barley germplasm as well as in the future for choosing parents from a variety of backgrounds for use in breeding programs.

Addressing the broader implications of AI–AI bias in decision-making systems

Accurate velocity estimation from surface-consistent residual statics

Reply to Bourguignon et al.: Convergence is a plausible hypothesis for Quina technology in East Asia

Correction: Flow partition in two-dimensional open channels with porous structures

IoT assisted fuzzy inference systems for intelligent 3D art design in movie animation scene design

Visual exposure to buildings in Switzerland: spatial patterns and changes over six decades

Abstract The visual presence of buildings in rural landscapes is a key yet often overlooked dimension of spatial development. This study presents a comprehensive visual exposure assessment of rural buildings, defined here as buildings located outside building zones, across Switzerland from 1960 to 2024, using binary and cumulative viewshed analyses. By integrating historical and contemporary building footprint datasets with digital height models and forest cover data, we quantify how visibility to buildings has evolved across five biogeographical regions. Results reveal a steady increase in visual exposure, primarily driven by construction in non-building zones, despite regulatory efforts to contain urban sprawl. The Plateau region exhibits the highest cumulative visibility due to its dense settlement and open terrain, while the Alpine regions maintain significant areas with minimal visual intrusion. Temporal patterns show that the most significant increases in visibility occurred between 1960 and 1980, aligning with broader trends of urban sprawl. The findings underscore the importance of incorporating visual exposure metrics into spatial planning and landscape monitoring frameworks. This study contributes a visibility-based perspective to the assessment of rural development and offers a valuable tool for evaluating the visual quality of changing landscapes.

Predicting the co-invasion of two Asteraceae plant genera in post-mining landscapes using satellite remote sensing and airborne LiDAR

Abstract The Asteraceae plant family includes the most widespread weedy invaders in Europe, which may jointly inhibit natural succession in degraded land under restoration. The complex local drivers of co-invasions hinder remote sensing (RS) monitoring efforts, as the links between the ecological and the spectral habitat properties are largely unknown. We proposed a comprehensive framework for machine learning modeling of the co-invasion of two Erigeron spp. and two Solidago spp. in post-mining landscapes of S Poland, using both field data and a combination of Sentinel-2, Landsat 7 and airborne LiDAR RS predictors. Stochastic Gradient Boosting best captured the non-linear dependencies (Accuracy = 0.670–0.886, AUC = 0.675–0.923), and generally outcompeted two other classifiers (Random Forest and Support Vector Machines with a Radial Basis Function Kernel). The field-based functional diversity metrics were the strongest predictors, corroborating improved resistance to invasions by native plant functional richness. In terms of RS data, the most favorable conditions for co-invasion were identified by a narrow range of reflectance in the red-edge interval of a Sentinel-2 image, and constrained by LiDAR-derived vegetation height (for Erigeron spp.) and by high land surface temperatures (for Solidago spp.). The highest share of patches suitable for co-invasion was consistently found in the low vegetation land cover class, between 36% and 64% cover. We therefore advise considering particular management actions, such as increasing the supply of native seed, thus improving local community resistance to invasions. The proposed methods and openly available RS predictors may facilitate targeted monitoring and cost-effective management interventions.

Reply to Yu et al.: Datasets, human judges, and future directions for evaluating AI–AI bias

An integrated algorithm for single lead electrocardiogram signal analysis using deep learning with 12-lead data

Abstract Artificial intelligence (AI) algorithms have demonstrated remarkable efficiency in analyzing 12-lead clinical electrocardiogram (ECG) signals. This has sparked interest in leveraging cost-effective and user-friendly smart devices based on single-lead ECG (SL-ECG) for diagnosing heart dysfunction. However, the development of reliable AI model is influenced by the limited availability of publicly accessible SL-ECG datasets. To address this challenge, presented study introduces a novel approach that utilizes 12-lead clinical ECG datasets to bridge this gap. We propose a hierarchical model architecture designed to translate SL-ECG data while maintaining compatibility with 12-lead signals, ensuring a more reliable framework for AI-driven diagnostics. The proposed sequential model utilizes a convolutional neural network enhanced with three integrated translational layers, trained on individual 12-lead clinical ECG, to significantly improve classification performance on SL-ECG. The experimental analysis is conducted using three benchmark datasets: Physikalisch-Technische Bundesanstalt (PTB-XL), Computing in Cardiology Challenge 2017, and China Physiological Signal Challenge 2018. This study also evaluates the effects of denoising techniques and lead polarity variations, including biphasic and negative deflections. Results show that the model achieved over 82% test accuracy on unseen SL-ECG signals, with an area under the receiver operating characteristic of 0.81, sensitivity of 76.60%, and specificity of 83.44% when trained on clinical lead I. Additionally, leads II, V4, and V5 demonstrated potential for effective AI model training. The work supports advancement of smart devices by enhancing SL-ECG classification and assists clinicians in assessing heart abnormalities more effectively.

The presence of Quina lithic technology in China 50 to 60 ka ago remains a hypothesis

Rolling bearing fault diagnosis in noisy environments using Channel-Time parallel attention networks

Abstract In Industry 4.0 intelligent manufacturing, rolling bearings serve as core components of rotating machinery. Their health status directly impacts the safety and reliability of entire manufacturing systems. However, existing fault diagnosis methods face critical challenges in noisy environments, including layer-wise feature information attenuation, insufficient multi-scale feature capture, and limited noise robustness. Such limitations create an urgent need for high-precision and robust deep learning diagnostic techniques. To address these challenges, this study proposes Channel-Time Parallel Attention Network (CT-ParaNet). The network innovatively designs a channel-time parallel attention mechanism that synchronously processes channel and temporal feature correlations to effectively solve information degradation in serial structures. The network constructs multi-scale parallel attention residual blocks using parallel multi-branch architecture with adaptive gating mechanisms to capture and fuse multi-scale fault features. Additionally, it establishes a serial-parallel hybrid processing architecture that systematically integrates parallel attention mechanisms with multi-scale feature extraction modules for hierarchical and parallel fine processing of fault signals. Experimental results on two independent bearing fault datasets show CT-ParaNet achieves accuracies of 98.53% and 98.29%, improving by 15.84 and 16.15% points over traditional methods respectively. Under extreme − 5dB signal-to-noise ratio (SNR) conditions, accuracies remain above 87% across Gaussian white noise, impulse noise, and colored noise environments. With only 0.1 training data ratio, accuracies exceed 92% on both datasets. CT-ParaNet significantly enhances accuracy and robustness of bearing fault diagnosis in noisy environments, providing important technical support for intelligent manufacturing equipment health monitoring.