Browse Articles

Correction: Seasonal variation in meat quality of Angus steers raised in a Mediterranean forage-fed system: A farm case study

Ion removal effect by soil remediation and desalination

Electrokinetic remediation of saline soils currently faces two primary constraints: high energy consumption and considerable variability in the removal efficiency of salts and base ions under differing treatment conditions. These limitations have hindered the widespread practical adoption of this technology.This study explores the Ion removal effect by soil remediation and desalination. Through laboratory simulations, the base ion removal effects and energy consumption under various electric field conditions were investigated. The results show that electrokinetic remediation at 2.0 V/cm can effectively remove base ions from the soil, with notable removal efficiencies of Na + and Cl - . It was also found that the electric field strength significantly affects both the remediation effect and energy consumption. An excessively high electric field strength may lead to a sharp decrease in soil moisture content, which in turn affects the remediation efficiency and increases energy consumption. Electrokinetic remediation has considerable potential for improving saline soils. By optimizing the electric field conditions and controlling pH levels, remediation can be enhanced while energy consumption is reduced. However, in practical applications, factors such as soil type and pollution degree must be considered, and further in-depth research is needed to refine and optimize remediation technology.

A frame of wideband wireless signal recognition and parameter extraction based on semantic segmentation

With the rapid development of wireless communication technologies, spectrum resources are becoming increasingly scarce, and spectrum monitoring technologies targeting control and interference suppression impose higher requirements on the real-time performance, reliability, and intelligence of signal detection, recognition, and key parameter extraction. Traditional signal processing methods heavily rely on operators’ prior knowledge, making it difficult to achieve intelligent spectrum monitoring, and often exhibit poor performance in complex electromagnetic environments with unknown signals or strong interference. Existing deep learning-based automatic modulation recognition techniques are more focused on signal recognition, with relatively limited research on detection and key parameter extraction. To address these challenges, this paper proposes a wideband signal processing frame based on semantic segmentation and signal spectrogram. The frame employs RepViT as the backbone network and achieves detection, recognition, and key parameter extraction of wideband signals through precise semantic segmentation of signal spectrogram. Experimental results on a large-scale synthetic dataset demonstrate that the proposed frame achieves a maximum signal recognition rate (mAcc) of 82.43% and an average signal recognition rate (aAcc) of 65.16% in multi-modulation scenarios and under different noise power levels. In terms of parameter extraction, the normalized root mean squared error (NRMSE) for time parameters (e.g., start time, and duration) is controlled within the ranges of 0.3%−2.8% and 0.4%−1.6%, respectively, while the NRMSE for frequency parameters (e.g., center frequency, and bandwidth) reaches 8.7% and 0.6% in multi-classification tasks, providing an effective reference solution for intelligent wireless signal analysis.

Retraction: A new compound of thiophenylated pyridazinone IMB5043 showing potent antitumor efficacy through ATM-Chk2 pathway

Bereavement practices and staff competencies on perinatal loss at a community-based teaching hospital

Objective Although considerable research exists on how families experience perinatal loss, the bereavement competencies and practices at our hospital were unknown. To investigate the baseline bereavement knowledge, skills, self-awareness, and organizational support among staff working at a community-based teaching hospital, a cross-sectional study was conducted using the validated Perinatal Bereavement Care Confidence Scale (PBCCS). Methods PBCCS scores range from 1 to 5 (1 representing Strongly Disagree and 5 representing Strongly Agree). Bereavement support confidence was calculated by using mean score for each subscale to obtain total PBCCS score (score range of 32–160). A sufficient level of confidence is considered 80% or higher. Independent t-tests/ANOVA measured differences in scores for two or more groups. Results A total of 46 survey responses were received; 19 were submitted from Labor and Delivery, 13 from the Emergency Department and 5 from Maternity. 28 respondents reported that they had not received any formal education on perinatal bereavement (p = 0.006). Scores for each subscale showed low levels of bereavement support knowledge (46.45 ± 13.04), skills (23.07 ± 7.31), self-awareness (24.85 ± 7.54) and organizational support (21.73 ± 6.08). The total mean sum score was 116 (72%), indicating inadequate levels of bereavement confidence overall. Those reporting having a formal education on perinatal bereavement care were more likely to have greater self-awareness as compared to those who did not (4.23 vs 3.39, p = 0.001). Conclusion Improving perinatal loss management in a hospital setting requires a comprehensive approach that includes data-driven assessment, staff education, support, and structured protocols. Key factors influencing compassionate, dignified, and holistic bereavement care provides nursing leadership with valuable information on educational gaps to target and when formalizing protocols or workflows to address this need.

The field factor: Industry publishing contribution and novelty in science

Firms frequently publish scientific articles as part of their R&D initiatives, motivated by commercial objectives. However, the extent of industry involvement in publishing varies across different scientific fields and can have implications for research within those fields. Novelty in science is associated with scientific and technological breakthroughs. In this paper, we examine a field-level antecedent of novelty—the extent of industry publishing contribution to a field—and its association with two key aspects of recombinant novelty of publications: the occurrence of a novel recombination ( novelty occurrence ), and the degree of novelty, captured through novelty breadth , reflecting the scope of novel integration of knowledge elements, and novelty distance , reflecting the extremity of conceptual divergence among novel knowledge recombinations. Drawing on a longitudinal dataset of 11.1 million publications across 1639 STEM fields from 2000 to 2014, we find that greater industry publishing contribution within a scientific field is associated with higher odds of novelty occurrence and greater novelty breadth , but lower novelty distance . Notably, university is an important driver of our results across the three dimensions of novelty highlighting the importance of industry publishing contribution in shaping the novelty of entire fields. In addition, we find that top-ranked research institutions appear better able to manage the trade-off between novelty distance and other forms of novelty as industry publishing contribution increases. Our findings emphasize the need for policymakers in encouraging and preserving more exploratory forms of novelty in fields with substantial industry publishing, where such exploration is particularly valuable.

Diet and gut microbiome of skipjack tuna (Katsuwonus pelamis) as indicators of environmental changes

Understanding the relationship between environmental changes and marine ecosystem dynamics is crucial, especially under the influence of climate events such as the El Niño Southern Oscillation (ENSO). The diet and gut microbiome of marine predators have the potential to efficiently, timely, and reliably indicate impacts of environmental and ecosystem changes, especially with the assistance of high-throughput sequencing (HTS) technology. This study investigated the gut content and microbiome of skipjack tuna ( Katsuwonus pelamis ) collected in the central Pacific Ocean during a transitional period of ENSO phases, shifting from a strong La Niña phase to a weak El Niño phase, aiming to evaluate the impacts of ENSO and other environmental factors on marine food webs and microbiome dynamics of skipjack tuna. While prey diversity was unaffected by ENSO events, skipjack tuna exhibited high diversity and opportunistic foraging patterns, with fish as the primary prey. In contrast, gut microbiome diversity was affected by ENSO events and Southern Oscillation Index (SOI). Five microbiome families (Fusobacteriaceae, Bacillaceae, Propionibacteriaceae, Beijerinckiaceae, and Comamonadaceae), which are associated with immune system functionality and nutritional provisioning of the host, displayed the most significant abundance changes between ENSO phases. A random forest model showed potential for ENSO phase classification based on the abundances of these five families, achieve high accuracy in internal validation, though the performance of external validation was mixed due to storage and sampling period differences. This study highlights the potential of skipjack tuna gut microbiome as indicators of rapid environmental changes, while acknowledging that the short sampling period requires longer-term validation across multiple ENSO cycles.

Gait kinematics and kinetics in patients with different grades of meniscus injury: A cross-sectional study

Meniscal injuries are common and can alter knee biomechanics, increasing the risk of osteoarthritis. This study investigated the effects of unilateral meniscal injuries of different Stoller grades on gait kinematics and kinetics. A total of 158 participants were stratified by MRI into three groups: control group(Grade 0, n = 51), Grade I–II (n = 54), and Grade III (n = 53). Three-dimensional motion capture synchronized with force platforms was used to assess peak sagittal-plane joint angles, joint moments, and ground reaction forces. Multivariate analysis of covariance was applied to adjust for body mass index, Lysholm score, and walking speed. Compared with healthy controls, injured participants demonstrated reduced knee flexion, hip extension, and lower extremity joint moments, along with increased ankle dorsiflexion, knee extension, and hip flexion angles; anterior, posterior, and lateral ground reaction forces were also significantly decreased. Although no significant differences in joint moments or ground reaction forces were observed between Grade I–II and Grade III groups, deviations in joint angles increased gradually with the severity of injury. The most pronounced changes were seen in the knee extension angle, which rose by 309.0% in Grade III compared with the control group, and the hip extension angle, which decreased by 53.3% in Grade III compared with the control group. A 16.5% reduction in the knee flexion angle was also observed. These findings indicate that even mild meniscal injuries produce substantial gait kinetic deficits, while kinematic alterations become more pronounced with higher-grade injuries. The study highlights the value of integrating Stoller grading with objective gait analysis to identify functional impairments not captured by patient-reported outcomes. This comprehensive approach provides a biomechanical basis for early assessment and individualized rehabilitation strategies, supporting knee function preservation and potentially slowing long-term degenerative changes.

Deep feature optimization using fusion of multiple self-supervised learning approaches and filter-based feature selection for lung cancer histopathology classification

One of the most deadly illnesses in the world is lung cancer, and increasing survival rates require early detection. Lung cancer diagnostics from the imaging modalities is always subjective, and this paves the way for deep learning assisted computer aided techniques. Still, the accuracy of such a technique is the major concern. This research work attempts to enhance lung cancer diagnostics from histopathological images using a previously unaddressed combination of multiple self-supervised learning techniques, filter-based feature selection, and Vision Graph Convolutional Networks. The main contribution lies in optimized feature fusion, and it brings complementary strengths of three different self-supervised learning approaches- contrastive alignment, redundancy reduction, and semantic grouping. The first step involves extracting the key features from histopathological images using a custom Convolutional Neural Network. Three complementary self-supervised learning methods – Deep Cluster, Bootstrap Your Own Latent, and Simple Framework for Contrastive Learning of Visual Representations – are then used to refine each of these features separately. Following optimization, the improved features are combined, and the most significant features from the combined set are found and preserved using a filter-based feature selection technique called Minimum Redundancy Maximum Relevance. Vision Graph Convolutional Neural Network is used as a classifier. Initial experiments are carried out on the 691 histopathological images extracted from the publicly available LungHist700 dataset. This dataset contains histopathological images under three categories: 151 normal subjects, 280 lung adenocarcinoma subjects, and 260 lung squamous cell carcinoma subjects. The proposed approach provided a balanced accuracy of 97% while the plain Vision Graph Convolution Neural Network offers only an 86% balanced accuracy score. Further validation is performed using two more publicly available datasets, namely LC25000 and TCGA UT datasets. The experimental results demonstrate the enhanced lung cancer prediction performance of the proposed approach in all three datasets.

Orthogonal Monomer Design Enables Sequentially Cured Hybrid Polymers for Ultralow‐Dielectric Applications

ABSTRACT Precise molecular design and orthogonal reaction pathways offer powerful tools for engineering polymer networks with controlled nanoscale architectures and advanced dielectric properties. Herein, we report an orthogonal monomer design strategy that integrates ring‐opening metathesis polymerization (ROMP), photodimerization, and thermally induced benzocyclobutene (BCB) crosslinking to construct hybrid polymers with sequentially cured, nanoconfined silsesquioxane (SSQ) domains. The modular incorporation of photoreactive anthracene and thermally active BCB units into ROMP‐compatible monomers enables chemically decoupled dual curing. This sequential crosslinking allows precise spatial confinement of SSQ domains within a covalent organic matrix, effectively minimizing interfacial defects and phase separation. The resulting hybrid networks exhibit an ultralow dielectric constant ( D k = 2.08), extremely low dissipation factor ( D f = 6.67 × 10 −4 ), high thermal stability (the 5% weight loss temperature, T d5 > 400°C), hydrophobic surfaces (water contact angle > 100°), and robust mechanical integrity. This work highlights a generalizable chemical design paradigm that links orthogonal monomer reactivity to nanoscale structure control and multifunctional dielectric performance, offering a promising pathway for advanced electronic packaging materials.

Author Correction: Dolutegravir restores gut microbiota in late-stage HIV-1 unlike darunavir: an open-label, randomized clinical trial

Enhancing microalgal productivity through bioactive substances, light, and CO2

Microalgae play a crucial role in ecosystems, from oxygen production to sustaining food webs and offering valuable applications in industry and environmental management. Increasing their productivity in terms of biomass yield, cultivation time, and nutritional or metabolite quality remains a major challenge. This study assessed the effects of 10 bioactive substances (including lactones, phytohormones, and natural extracts), four light wavelengths, and four CO₂ injection regimes on Arthrospira platensis , Chlorella vulgaris , Ankistrodesmus falcatus , and Tetradesmus dimorphus , using linear modeling and LSD (Least Significant Difference) post hoc tests. N-butyryl-DL-homoserine lactone significantly enhanced A. platensis growth (94.4%), while naphthaleneacetic acid and indole-3-butyric acid promoted T. dimorphus growth (138.1% and 115.5%, respectively). More accessible alternatives, such as Aloe vera and coconut water, also stimulated growth: A. platensis , C. vulgaris , and A. falcatus increased by 85.3%, 69.2%, and 87.7% with Aloe vera , while T. dimorphus increased 80.5% with coconut water. Regarding light quality, red light (600–700 nm) benefited A. platensis and A. falcatus (49.2% and 20.8%, respectively), whereas blue light (400–490 nm) favored C. vulgaris and T. dimorphus (57.7% and 31.5%, respectively). CO₂ injection further enhanced biomass production and carbon fixation, particularly in C. vulgaris and A. falcatus (73.5% and 53.5%, respectively). However, combined treatments did not produce additive effects, suggesting complex interactions. Overall, these findings demonstrate the potential of bioactive substances and environmental conditions to improve microalgal performance and highlight the importance of investigating synergistic effects and scalability for large-scale production.

Single-bacterium metabolome revealing heterogeneous cellular states in bacterial populations

Baseline RDW combined with dynamic trajectory: Predictive value for 30-day all-cause mortality in patients with sepsis-induced coagulopathy and development of a nomogram

Objective Sepsis-induced coagulopathy (SIC) is associated with high mortality. This study aimed to explore the predictive value of baseline red blood cell distribution width (RDW) and its dynamic trajectory for 30-day all-cause mortality in SIC patients, and to develop a practical nomogram. Methods A retrospective cohort study was conducted on 2531 SIC patients from the MIMIC-IV v3.1 database. Patients were grouped by baseline RDW tertiles, and RDW dynamic trajectories were constructed via Latent Class Growth Mixture Model (LCGMM). Kaplan-Meier analysis, Cox proportional hazards regression, and Restricted Cubic Spline (RCS) model were applied to assess the association between RDW and 30-day mortality. A nomogram was built via Boruta algorithm and Lasso regression, with external validation in 317 patients from a Shanghai tertiary hospital. Results 30-day mortality increased with elevated baseline RDW (Q1: 4.5% vs. Q2: 10.7% vs. Q3: 22.7%, P  < 0.001), and Q3 was an independent risk factor (adjusted HR = 2.666, 95%CI: 1.854–3.834). RDW> about 15% correlated with sustained mortality risk. LCGMM identified two trajectories (stable low-level Traj0, rapidly ascending Traj1), with Traj1 showing higher mortality (31.1% vs. 10.0%, adjusted HR = 2.522). The nomogram integrating RDW and clinical indicators demonstrated good discrimination (C-index = 0.805, AUC = 0.813) and utility. Conclusion High baseline RDW and rapidly ascending RDW trajectory are independent risk factors for 30-day mortality in SIC patients. The nomogram enables convenient and accurate risk stratification and prognostic evaluation.

Proteome-wide prediction of the functional impact of missense variants with ProteoCast

Molecular architecture of the fungal-specific potassium channel TOK1

Determination of Polymorph A‐Enrichment and Absolute Structure of Chiral Zeolite Beta Through Electron Crystallography

ABSTRACT Zeolite Beta, one of the earliest recognized chiral zeolitic systems and widely used in the chemical industry, poses a significant challenge for characterizing its chiral structure because of its intergrowth of polymorphs. Herein, we report a comprehensive electron crystallography framework for the determination of polymorph ratios, absolute structures, and enantiomeric distributions in zeolite Beta. By developing an optimized real‐space imaging protocol based on scanning transmission electron microscopy, we circumvent the mechanical tilting limits of electron microscopy, enabling chiral identification even at arbitrary crystal orientations. Furthermore, we introduce a weighted refinement strategy for 3D electron diffraction (3D ED). By utilizing dynamical diffraction simulations to derive weighting factors from enantiomeric intensity disparities, the sensitivity of absolute structure determination is significantly enhanced, overcoming the dilution of chiral signals by centrosymmetric components. Experimental validation confirms that this integrated approach accurately quantifies regional polymorph A enrichment and successfully discriminates between P 4 1 22 and P 4 3 22 enantiomers. This work not only addresses long‐standing challenges in characterizing intergrown chiral zeolites but also provides a refined methodological paradigm for investigating such complex porous materials.

Enhanced elastocaloric cooling beyond Clausius–Clapeyron limits

Abstract The elastocaloric effect, driven by stress-induced martensitic transformations, offers a promising route toward efficient and environmentally friendly solid-state cooling. However, its practical implementation has been hindered by an inherent trade-off: materials exhibiting large isothermal entropy changes typically operate over narrow temperature windows, thereby limiting their overall cooling performance. Here, we demonstrate an elastocaloric response in a Ti–Al–Cr superelastic alloy that overcomes this limitation. Direct measurements reveal a pronounced elastocaloric cooling effect over an ultra-wide temperature range of 305 K, from 97 K to 402 K. This temperature span exceeds that predicted by the Clausius–Clapeyron relationship (235 K), indicating a significant deviation from conventional thermodynamic expectations. At room temperature, a large adiabatic temperature change of ~10 K is directly measured, corresponding to a cooling output of 5.76 J·g⁻ 1 and a material coefficient of performance of 4.6, demonstrating competitive cooling performance at practical operating conditions. In addition, the elastocaloric response is maintained over the entire temperature range despite the expected decrease in entropy change at lower temperatures, indicating that the conventional trade-off between temperature span and cooling strength is effectively mitigated. This exceptional behavior originates from a combination of anomalous temperature dependence of the critical stress for martensitic transformation and high mechanical strength, which together enable fully reversible stress-induced transformations across a broad thermal domain. Our findings reveal a new regime of elastocaloric behavior and establish a guiding principle for overcoming the apparent limitations imposed by Clausius–Clapeyron-based descriptions in caloric materials.

Evidence for Itinerant Ferromagnetic Flat Bands Producing Large Transverse Responses

ABSTRACT Interference of electronic wavefunctions may result in the emergence of a flat band, and trigger nontrivial correlated phenomena and phase formations when the flat band crosses the Fermi energy . To date, such an itinerant flat band at has been reported for electronic states without symmetry breaking. If a flat band arises at even with broken symmetry, the ordered state may offer a distinct platform for studying novel phases and spontaneous responses. Here we report experimental and theoretical evidence for itinerant ferromagnetic flat bands formed by spin‐polarized ‐electron orbitals on a stacked honeycomb–kagome lattice, hosting the alternating stack of honeycomb and kagome sublattices. Our theory together with angle‐resolved photoemission spectroscopy and magneto–thermoelectric measurements finds multiple topological flat bands at generating large Berry curvature in the ferrimagnet below its Curie temperature of 940 K. We observe large transverse responses, in particular, gigantic anomalous Nernst effect producing the largest transverse thermoelectric conductivity over 10 A at room temperature. Our study paves a path for developing itinerant magnetic flat bands and to their spintronic and thermoelectric applications.

Reprogramming of bacterial virulence by lysine acetylation

Abstract Gram-negative bacteria use a plethora of virulence factors to infect eukaryotic cells. CE-clan protease-related virulence factors were reported to act as deubiquitinases/ubiquitin-like specific proteases. Some have an additional acetyl-transferase activity. The molecular mechanisms underlying this dual activity and the physiological consequences are only marginally understood. Here, we report crystal structures for the Simkania negevensis virulence factor SnCE1 in apo-states and in complex with SUMO1. We confirm SnCE1 acting as an efficient deSUMOylase and discover an intrinsic autoacetyltransferase activity. Acetylation impairs SnCE1 tetramer formation structurally being incompatible with SUMO1 binding. We provide a model for regulation of SnCE1-mediated virulence by lysine acetylation modulating autoproteolytic processing and its subcellular distribution in the host cell. SnCE1 localizes to the endoplasmic reticulum in human cells and increases fragmentation of mitochondria. Our data provide mechanistic insights into how lysine acetylation of virulence factors is used to reprogram virulence adjusting it to the host cells’ metabolic state.