Browse Articles

High-fidelity modular skeletons authenticate a Cambrian origin for Bryozoa

Abstract The major animal body plans originated during the Cambrian explosion, yet the phylum Bryozoa has remained a conspicuous exception to this pattern 1 . The initial discovery of Protomelission gatehousei 2 provided compelling evidence for a Cambrian origin for the Bryozoa, together with other major metazoan phyla and compatible with independent molecular clock estimates 3–7 . Nevertheless, the scarcity of definitive soft-tissue anatomy and diagnostic skeletal microstructure has left its phylogenetic affinities ambiguous and debated 8,9 . Here we report exquisite fossils of P. gatehousei and a new taxon, Dayingomelission hexaclitia gen. et sp. nov., from the early Cambrian Xiannüdong Formation of China. These specimens preserve in situ phosphatized soft tissues in modular skeletons, revealing critical anatomical structures, including styles, annular muscles, membranous sacs and ring septa. This suite of traits provides definitive evidence that these taxa belong to the Bryozoa. Phylogenetic analysis incorporating these new features identifies them as crown group stenolaemates. These results confirm a Cambrian origin for the phylum and reveal an unexpected early disparity in colonial architecture, demonstrating that bryozoan diversification was an integral component of the Cambrian radiation. Moreover, the early appearance of a differentiated stenolaemate crown group indicates a still deeper origin for the bryozoan stem lineage than was first apparent.

ENCORE: an energy-efficient cluster-based multi-hop routing framework for WNSNs in IoNT

Ferron-driven photoferroic hysteresis in van der Waals CuInP2S6

Research on an improved RT-DETR-based model for rice disease detection

Monitoring and precisely localizing rice diseases is essential for agricultural productivity and food security. Existing detection methods face challenges such as high computational complexity, semantic information loss, difficulty detecting small targets, and limited robustness. To address these issues, this study proposes ECL-RTDETR, an enhanced RT-DETR–based rice disease detection model. First, a lightweight EfficientViT backbone is employed for feature extraction, incorporating a streamlined multi-head self-attention module to improve inference speed, reduce computational cost, and strengthen local feature extraction. Second, the CARAFE upsampling operator is introduced to better preserve detailed feature information without added computational burden, enhancing fine-grained representation. Finally, standard convolution in the neck network is replaced with LDConv (lightweight dynamic convolution) to enable adaptive feature learning under complex conditions, addressing variations caused by illumination, occlusion, and disease diversity. Experimental results show that ECL-RTDETR improves mAP@0.5 by 0.7%, increases detection speed by 22.2 FPS, and reduces computational cost by 81.8 GFLOPs and parameters by 22.12M compared with the baseline RT-DETR. Overall, ECL-RTDETR delivers superior accuracy, speed, and efficiency, offering a robust solution for intelligent rice disease detection and localization, and advancing smart agriculture and sustainable food security.

Tri-MCA fusion: cross-modal attention and dynamic gating for multimodal sentiment analysis

Double volcanic tracks at Hawaii caused by bridgmanite-enriched primordial mantle blobs

Reliability assessment of key equipment for coal gasification using artificial intelligence technology

To address the gap in quantitatively modeling dynamic failure mechanisms for Gasifier lock bucket valve system reliability, this study proposes an innovative method: using backpropagation (BP) neural network to optimize the prior data of dynamic Bayesian network (DBN). Firstly, based on the empirical formula for the number of hidden layer neurons, the original DBN model of the system is adapted to a structurally adaptive BP neural network to calibrate its prior parameters，and the correspondence between the prior distribution of DBN and the input-output functions of the BP network is established. Subsequently, utilizing the core characteristics of BP network, iterative optimization of DBN prior data is achieved through continuous learning of the operating performance of the lock bucket valve system. Next, the optimized DBN model is subjected to dynamic system reliability evaluation using bidirectional inference analysis. The results show that in the positive prediction, the reliability of the system after 300 hours of operation without considering maintenance is only 0.047, which can be improved to 0.302 after incorporating maintenance factors. The reliability of the optimized system is lower than before optimization, and the gap gradually widens over time. Reverse reasoning clearly identifies the weak links in the system as high-pressure coal powder flushing, adhesion between ball seats, internal deformation and wear. Targeted preventive measures can improve the reliability of the system and extend its service life.

Computational design of artificial supply networks for engineered human tissue

Abstract The development of large-scale, three-dimensional human tissues is crucial for various applications in therapeutic tissue engineering, disease modeling, and drug testing. However, due to the diffusion limit of oxygen, the lack of functional vascular networks is a significant limitation in maintaining these engineered tissues in the laboratory. To address this challenge, we present a systematic, model-based design process for artificial supply networks that can ensure a sufficient supply of oxygen and nutrients to engineered human tissue. Our approach combines mathematical models of fluid dynamics, cell metabolism, and network properties to identify key parameters influencing the supply performance. We demonstrate the applicability and possibilities of this design process by simulating different network structures, including cuboid and rhombic dodecahedral honeycombs, under various conditions. Our results show that the structure of the artificial supply network, oxygen concentration, and solute flow within the network strongly influence cellular metabolic activity and viability. We also examine the effects of non-uniform cell density, channel blockage, and long channel length on the oxygen distribution inside the cell-containing tissue compartment. Our findings highlight the importance of considering these factors in the design of artificial supply networks for large-scale engineered human tissues. This study provides a promising approach for quickly exploring the vast design space of possible network structures under different conditions for desired cell and tissue states, ultimately contributing to the development of more efficient and effective tissue engineering strategies.

Tonotopic specialization of MYO7A isoforms in auditory hair cells

The UN SDGs as a global ‘directive shift’ and the institutionalization of sustainability research

This paper examines how the UN Sustainable Development Goals (SDGs) shape the institutionalization of sustainability research within scholarly publishing. We argue that the SDGs operate as a globally endorsed form of external research agenda-setting, constituting a “directive shift” in science. Focusing on SDG 04 (Quality Education), SDG 08 (Decent Work and Economic Growth), and SDG 13 (Climate Action), we analyse changes in Scopus-indexed journals from 1990 to 2024. Using large-scale bibliometric data, we classify ( n  = 30,604) journals by activity level, age (newborn, young, mature, established), disciplinarity, publishing model, and long-term survival across publication thresholds ( k  = 1, 3, 5, 10). Results reveal a sustained increase in journal participation related to SDG-related publishing, with pronounced entry surges around major international agreements in 2005 and 2015. Participation is driven primarily by young and mature journals, while established journals contribute a comparatively small share of new entrants. Further analysis of established titles reveals that top-ranked (Q1) core journals are more prominent in SDG 13 than in SDG 04 and SDG 08, suggesting uneven integration across disciplinary hierarchies. Multidisciplinary and open-access journals dominate entry patterns, and survival rates increase at higher publication thresholds, indicating sustained engagement over time. Overall, these structural dynamics suggest that the SDGs operate as a directive shift, contributing to the progressive consolidation of sustainability research within the journal system.

Deep learning-based Desikan-Killiany parcellation of the brain using diffusion MRI

Abstract Accurate brain parcellation in diffusion MRI (dMRI) space is essential for advanced neuroimaging analyses. However, most existing approaches rely on anatomical MRI for segmentation and inter-modality registration, a process that can introduce errors and limit the versatility of the technique. In this study, we present a novel deep learning-based framework for direct parcellation based on the Desikan-Killiany (DK) atlas using only diffusion MRI-derived data. Our method utilizes a hierarchical, two-stage segmentation network: the first stage performs coarse parcellation into broad brain regions, and the second stage refines the segmentation to delineate more detailed subregions within each coarse category. We conduct an extensive ablation study to evaluate various diffusion-derived parameter maps, identifying a top-performing combination of fractional anisotropy, trace, sphericity, and maximum eigenvalue that enhances parcellation accuracy compared with previously used parameter choices. When evaluated on the Human Connectome Project, our approach achieves higher Dice Similarity Coefficients compared to existing state-of-the-art methods. On the Consortium for Neuropsychiatric Phenomics dataset, where reliable voxel-wise DK reference labels in diffusion space are not available, our method demonstrates label-free evidence of robustness across different image resolutions and acquisition protocols by producing more homogeneous parcellations as measured by the relative standard deviation within regions. This work represents a step toward more practical dMRI-based brain parcellation by avoiding the need for anatomical MRI and subject-specific anatomical-to-diffusion registration at inference time. The implementation of our method is publicly available on https://github.com/xmindflow/DKParcellationdMRI .

Catalytic nano-metal interfaces drive pH-universal CO2-to-ethanol conversion

The supportive care needs of Iranian couples during postpartum hospitalization: A protocol of design, implementation and evaluation of intervention

Background The immediate postpartum period is marked by significant physiological and hormonal changes, which may present mothers with various social, emotional, and functional challenges. The goal of optimal postpartum hospice care is to sustain and enhance the health of both mothers and newborns while fostering a supportive environment for families and communities to address diverse health and social needs. Implementing comprehensive supportive care programs that offer full coverage of services for women can ensure that their needs are adequately met during this critical postpartum phase. Consequently, this study aims to investigate the supportive care requirements of couples during the postpartum period using a mixed-methods approach, along with the design and implementation of a needs-based intervention to improve health services during this essential time. Methods This study employs a multistage mixed-methods approach, structured in a sequential exploratory design consisting of three distinct phases. Initially, an exploratory qualitative study will be conducted utilizing a conventional content analysis framework to investigate the supportive care needs of couples during the postpartum hospitalization period. The second phase will involve a nominal group meeting in which the concerns and supportive care requirements identified by couples will be discussed in the presence of reproductive health specialists, policymakers, and experts. This collaborative effort will facilitate the prioritization of these needs. Following this prioritization, a comprehensive review of interventions and programs addressing couples’ supportive care needs during the postpartum hospitalization period on a global scale will be undertaken. Finally, after another panel of experts, the best intervention in this regard will be designed and consisted of a quantitative clinical trial aimed at evaluating the effectiveness of the intervention on the supportive care needs of Iranian couples during the postpartum period. Discussion The results of this study can lead to the design of a comprehensive supportive care program with comprehensive coverage of couples’ needs during the postpartum hospitalization period. The study aims to ensure that couples’ needs are met in this critical period. If this program is effective, it can be included in postpartum health care guidelines. Clinical trial registration No. IRCT20110621006854N8 (2024-11-16)

Ubiquitin tags detected on non-protein biomolecules using new method

Microbial evaluation of zirconia and titanium implants in the anterior mandibula: a randomized controlled clinical trial

Abstract To evaluate the effect of the implant material—either titanium or zirconia—on the development of bacterial deposits. In this one-year prospective split-mouth study, 20 patients with an edentulous lower jaw were treated with two zirconia and two titanium implants. Clip attachments were used for the removable denture. During the follow-up period, fluid samples were taken from around each patient’s implants and natural teeth to analyze the microbiota using DNA isolation, amplicon sequencing, and downstream analysis. Between the different time points, for the titanium material, the relative abundance of Actinomyces israelii increased significantly (p = 0.009) after 12 months when compared with the baseline. When comparing the different time points for each material, a significant decrease in the phyla Fusobacteria (p = 0.03) and Proteobacteria (p = 0.03) after six months versus baseline was detected for only zirconia, whereas the abundance of Firmicutes was significantly increased (p = 0.03). When comparing the different materials at each time point, the phylum Actinobacteria was found to be less abundant after 12 months on the zirconia implants than on the titanium implants (p = 0.04). By contrast, on the zirconia material, the relative abundance of the genus Leptotrichia decreased significantly after six months when compared with the baseline, while no significant changes in terms of this genus were found for the titanium implants or teeth over time. Zirconia tended to show less abundant bacterial deposits over time. The microbial diversity was lower on the titanium implants than on the ceramic material after 12 months. In addition, after 12 months of evaluation, neither the teeth nor the implants showed similar prevalences or levels of the target species.

Transgenic hookworm secretes anti-tetrodotoxin human single chain antibody

Antenatal education for labour and postpartum pain: A scoping review of content, delivery approaches, evidence gaps, and lived experiences

Background Pain during labour and the postpartum period is a complex and multidimensional experience. Antenatal education programmes aim to prepare individuals for childbirth and early parenthood; however, the extent to which these programmes address labour and postpartum pain management, and how women experience this education, remains unclear. This scoping review aimed to map the content, delivery characteristics, and evidence gaps of antenatal education programmes addressing labour and postpartum pain, including women’s lived experiences. Methods This review was conducted in accordance with PRISMA-ScR and Joanna Briggs Institute guidelines. The protocol was registered with the Open Science Framework (6597j). Twelve electronic databases were systematically searched in November 2025. Quantitative, qualitative, and mixed-methods primary studies examining antenatal education programmes with a focus on labour or postpartum pain were included. A narrative synthesis was undertaken to map intervention content, delivery approaches, and pain-related outcomes and experiences. Results A total of 5,959 records were identified from the search strategy. A total of 17 articles met the eligibility and inclusion criteria, including seven randomised controlled trials, seven quasi-experimental studies, one pre-post study, and two qualitative studies. The content and structure of antenatal education interventions between studies was heterogenous. Common themes included the distinction between “true and false labour pain” and breathing exercises. Qualitative findings highlighted women’s perceived improvements in pain coping, confidence, sense of control, and use of non-pharmacological strategies during labour. Conclusion Antenatal education programmes contain limited information on labour and postpartum pain management, with little consistency across interventions. While non-pharmacological strategies appear valuable in supporting coping and confidence during labour, pain mechanisms and postpartum pain remain under-addressed. Incorporating pain-focused education such as pain neuroscience principles may enhance antenatal education and support more effective pain management. Further research is required to develop and evaluate consistent, evidence-based antenatal education approaches that address both labour and postpartum pain.

Investigating the structure of the greylag goose vocal repertoire: what can unsupervised methods tell us?

Abstract Defining a comprehensive signal repertoire is an important step to understanding a species’ vocal communication system. Here, we investigated the vocal repertoire of a well-investigated model species in ethology: the greylag goose ( Anser anser ). We applied unsupervised machine learning algorithms to a large dataset of vocalisations from a free-living population of greylag geese to investigate the acoustic structure of this species’ vocal signals. We extracted four types of data representations, which were projected into 2, 20 and 100 dimensions using the UMAP algorithm, and then grouped using two commonly used clustering methods. Additionally, we successfully applied a graph-based clustering approach — Leiden community detection — which, to our knowledge, has not previously been employed in bioacoustics. Our analyses revealed a partly graded vocal repertoire that broadly matched early descriptions of the greylag goose call repertoire. Audio feature vectors, rather than more commonly used spectrographic representations, revealed clusters most congruent with human labels and offered the most comprehensive visualisation of the acoustic space. Leiden community detection performed comparably to established approaches but matched the number of human-defined classes closest. These findings highlight the impact of data representation on repertoire analysis and provide the first objective, quantitative characterisation of the greylag goose vocal repertoire.

In vivo dynamic hotspot-enhanced Raman spectroscopy via reconfigurable swarming nanoprobes

Comparative unfolding of the Trp-cage miniprotein in anionic and cationic surfactants

This study investigates the effects of anionic sodium dodecyl sulphate (SDS) and cationic cetyltrimethylammonium bromide (CTAB) surfactants on the stability of the Trp-cage miniprotein in aqueous solution at varying concentrations and temperatures. Conformational dynamics were analyzed using principal component–based free-energy landscapes, cluster population analysis, and radial distribution functions. The results show that at 25 °C in water, the protein adopts a compact native basin, whereas at 100 °C it exhibits expanded conformational space with multiple metastable states. The presence of surfactants further modulates this behavior in a concentration-dependent manner. Cluster population analysis shows that SDS promotes a highly heterogeneous ensemble characterized by reduced dominance of the native-like cluster, while CTAB partially protects the protein from thermal denaturation at higher concentrations. Radial distribution functions demonstrate strong accumulation of SDS headgroups around the protein and pronounced insertion of SDS alkyl tails into hydrophobic protein regions, indicating direct hydrophobic destabilization and micelle-assisted unfolding. In contrast, CTAB exhibits weaker headgroup association owing to electrostatic repulsion and reduced tail–hydrophobic contacts, suggesting a less disruptive interaction mechanism. At high concentration, CTAB aggregates provide a structured hydrophobic environment that stabilizes the folded state and suppresses denaturation. Together, these results provide a molecular-level picture of how surfactant chemistry and concentration govern the conformational stability of a cationic protein, highlighting the dominant role of hydrophobic interactions in surfactant-induced denaturation at high temperature.