Browse Articles

Unconventional‐Phase Pd ₂₀ Te ₇ Octahedra With High‐Index Facets for Oxygen Reduction

ABSTRACT The high surface energy leads to a significant challenge in the chemical synthesis of high‐index faceted nanomaterials. While the preparation of unconventional‐phase palladium (Pd)‐based nanocrystals with high‐index facets is largely unexplored. Herein, we introduce ethylene glycol as the morphology and size controller for unconventional‐phase Pd 20 Te 7 nano‐octahedra (NOs) enclosed by {300}, {20}, and {23} high‐index facets. These facets endow Pd 20 Te 7 NOs with excellent electrocatalytic activity for alkaline oxygen reduction reaction. Their high mass activity of 1.91 A mg Pd −1 at 0.90 V (vs. RHE) demonstrates a 1.89‐fold enhancement relative to Pd 20 Te 7 nanoparticles (NPs). More importantly, the Pd 20 Te 7 NOs‐based membrane electrode assembly (MEA) achieves a peak power density of 0.59 W cm −2 and a mass activity of 14.33 A mg PGM −1 (PGM: platinum group metals), measured in H 2 ‐O 2 fuel cells at an internal resistance‐corrected voltage of 0.65 V, corresponding to a 1.22‐fold and 2.47‐fold enhancement compared to Pd 20 Te 7 NPs (0.49 W cm −2 and 5.81 A mg PGM −1 ). Furthermore, the Pd 20 Te 7 NOs‐based MEA demonstrates stable operation at 0.4 A cm −2 for nearly 1440 min under an ultralow Pd loading of 0.022 mg cm −2 on the cathode. This work offers a new avenue to rationally design and construct high‐index faceted nanocrystals for high‐efficiency catalytic applications.

Biobehavioral synchrony between three to four months old infants and psychologically healthy mothers during the face-to-face still-face paradigm

Abstract Human social interaction emerges through coordinated behavior, emotion, and physiology. Biobehavioral synchrony supports stress regulation, attachment, and socio-emotional development, yet its manifestation in psychologically healthy mother-infant dyads remains poorly understood. In particular, little is known about how behavioral and physiological modalities interact and how synchrony varies across contexts. To address this, 94 mother-infant dyads with 3-4-months-old infants participated in the Face-to-Face Still-Face paradigm while cardiac activity was recorded simultaneously in mothers and infants. Physiological synchrony was quantified using cross-correlations of heart rhythm and heart rate variability (HRV) time series, averaged in 10-second windows. We coded behavioral synchrony and assessed bonding via maternal self-report. Behavioral synchrony was higher during play but declined after the stressor, while physiological synchrony was primarily evident during play and lagged 10 s, with maternal responses following infant signals. Low behavioral synchrony corresponded to stronger negative physiological synchrony, indicating that physiological co-regulation persisted despite behavioral misalignment. Supporting theories of early co-regulation, higher behavioral synchrony was linked to fewer bonding difficulties. These findings suggest that low-risk mothers show coordinated physiological fluctuations with their infants, highlighting maternal responsiveness as a key co-regulatory mechanism, providing a framework to identify atypical parent-infant dynamics and guiding parenting interventions that support child development.

Inside Front Cover: Modular Assembly of Bioconjugates Enabled by a Pyridine‐Based Chemoselective Sequential Conjugation Platform

Conductive polyaniline coupled ZnBi2S3 nano chalcogenide for electrochemical detection of ciprofloxacin and photocatalytic degradation of antibiotics

Correction to “Rabies Virus‐Inspired Metal–Organic Frameworks (MOFs) for Targeted Imaging and Chemotherapy of Glioma”

Neuroprotective curcuminoids from Curcuma longa as multi-target-directed ligand therapeutics for Alzheimer’s disease: an integrated in silico and in vivo approach

Au Single‐Atom Interlayer Bridge Promotes Cross‐Layer Charge Transport and Intrinsic Catalytic Dual‐Sites Activation for Efficient CO ₂ Photoreduction

ABSTRACT Layered photocatalysts are attractive for artificial photosynthesis, but their performance is often limited by inefficient charge transport across interlayer gaps and sluggish surface reaction kinetics. Herein, we propose a single‐atom interlayer bridge strategy by introducing Au single atoms into layered Bi 4 O 5 Br 2 (AuIB‐BOB) to address the above limitations. The incorporated Au atoms substitute Bi sites and are stabilized by a mixed O/Br coordination environment with an average local structure of Au 1 O 3 Br 2 , forming a covalent bridge between the Br − and [Bi 4 O 5 ] 2+ layers to provide an excellent pathway for cross‐layer charge transport. Thus, holes that would otherwise remain confined in the Br − layer are extracted and delivered to the surface O‐oxidation sites, while photogenerated electrons are retained and utilized at the surface Bi‐reduction sites. This directional charge redistribution considerably suppresses charge recombination, prolonging the average carrier lifetime from 19.5 to 109.7 ps. Meanwhile, the bridge‐mediated charge transport activates the intrinsic Bi reduction sites and O oxidation sites synchronously, promoting CO 2 adsorption/activation and H 2 O oxidation‐related processes. Without sacrificial agents or photosensitizers, AuIB‐BOB achieves a CO 2 ‐to‐CO evolution rate of 58.21 µmol g −1 h −1 in pure water. This work provides an atomic‐level paradigm for regulating cross‐layer charge transfer and unlocking intrinsic redox sites in layered photocatalysts.

ST-gaussian: improved 3D content generation based on stable diffusion and gaussian splatting

Outside Back Cover: Cycloazulenylene

Machine learning-guided rapid virtual screening and molecular dynamics validation of compounds against select Plasmodium falciparum targets

Bioleaching of olivine and enstatite with formation of Mg-oxalate mediated by engineered Gluconobacter oxydans

Abstract Carbon mineralization using ultramafic rocks is a promising approach for long-term carbon dioxide removal. Here, we investigate whether a genetically engineered strain of Gluconobacter oxydans (B58 ∆pstS, P112:mgdh) can simultaneously achieve carbon mineralization and bioleach critical elements. Olivine and enstatite were bioleached at low-temperature conditions (30 °C) and with 5% pulp density. Direct G. oxydans -mineral contact promotes Fe 2+ oxidation and leads to higher leaching efficiency compared to leaching with a cell-free biolixiviant. Importantly, G. oxydans facilitates the precipitation of magnesium oxalate, a compound with twice the carbon storage capacity of magnesite. Oxalic acid was detected in the G. oxydans -produced biolixiviant, and solid-phase Mg-oxalate formed most efficiently at low pH. SEM and XRD analyses reveal extensive olivine dissolution and secondary coating by Mg-oxalate and amorphous silica, which may inhibit further leaching. Mass balance calculations show that G. oxydans leached up to 75% of the Mg hosted in the starting materials, while only 11% of leached Mg reacted to sequester carbon as Mg-oxalate after 15 days. The enhanced sequestration potential of Mg-oxalate combined with bioaccelerated critical element leaching to offset costs represents a promising opportunity for global carbon storage that is worthy of further investigation. (193/200)

Plasma neuropilin-1 is associated with a favorable lipid profile and insulin sensitivity in a population-based sample of 50-year-old individuals

Abstract Membrane-bound neuropilin-1 (Nrp-1) has been implicated as a regulator of insulin resistance-related lipid metabolism, but little is known about its circulating form, soluble Nrp-1 (sNrp-1), in a metabolic context. Here we investigated the association between plasma levels of sNrp-1 and metabolic variables in 501 participants from the Prospective investigation of Obesity, Energy and Metabolism (POEM) study. Metabolic phenotyping included blood sampling, dual energy X-ray absorptiometry scan, magnetic resonance imaging for fat distribution, and a glucose tolerance test. Associations were examined using linear regression models and Spearman’s rank correlation. Plasma sNrp-1 levels were inversely associated with levels of total fatty acids, triglycerides and lactate, and positively associated with markers of insulin sensitivity. In human Simpson-Golabi-Behmel Syndrome (SGBS) adipocytes, we showed that treatment with recombinant Nrp-1 reduced phosphorylation of hormone-sensitive lipase in the basal state and attenuated isoproterenol-stimulated glycerol release, a measure of total lipolytic activity. Based on these findings, we thus postulate that sNrp-1-mediated suppression of lipolysis could explain our observed association between plasma sNrp-1 and a favorable lipid profile in humans. Further studies are warranted to establish causality and to clarify the role of sNrp-1 in promoting insulin sensitivity in human adipose tissue.

Assessing the learning curve in laparoscopic TAPP repair for emergency inguinal hernia: a comparative study with open repair

Abstract Emergency inguinal hernia repair presents considerable surgical challenges, with a continuous debate regarding the best approach. This study analyzes the outcomes of laparoscopic transabdominal preperitoneal (TAPP) repair in comparison to open tension-free repair, while also assessing the learning curve associated with TAPP. A retrospective study was conducted on 71 patients undergoing emergency inguinal hernia repair (33 TAPP, 38 open). Outcomes included operative time, intraoperative complications, pain scores, hospital stay, return to activity, recurrence, and learning curve analysis comparing early (first 18) vs. late (last 15) TAPP cases. Operative time was longer with TAPP (98.3 ± 9.4vs.80.3 ± 11.4 min, p  = 0.006), with a 6.1% conversion rate. Visceral injuries were more common in TAPP (15.2%vs.2.6%, p  = 0.058), while vascular injuries were higher in open repair (10.5%vs.3.0%, p  = 0.218). TAPP had significantly lower pain scores at 6, 12, and 24 h ( p  < 0.05), shorter hospital stay (2.5 ± 0.5vs.4.8 ± 0.8 days, p  = 0.001), and earlier return to activities (5.5 ± 0.5vs.10.5 ± 1.0 days, p  = 0.001). Recurrence and readmission rates were comparable. Later TAPP cases showed shorter operative time (93.4 vs. 102.4 min, p  = 0.002), no conversions, and better pain control. TAPP is a feasible emergency repair option, for selected cases, with a clear learning curve which improves outcomes after 15–18 cases. However, larger prospective studies are required to confirm these findings

Species distribution modelling of Ornithodoros spp. in California with consideration of climate variation and identification of TBRF, EBA and ASFV vector–host interfaces

Abstract Predicting the distribution of Ornithodoros ticks is essential for understanding the spatial dynamics of tick-borne disease risk under environmental change. We developed species-specific distribution models for Ornithodoros coriaceus , Ornithodoros hermsi , and Ornithodoros parkeri across California using an ensemble approach combining Maximum Entropy (MaxEnt) and Random Forest (RF) algorithms. Habitat suitability was estimated under present conditions and future climate scenarios (2061–2080), and binary maps were generated using model-specific thresholds to quantify changes in suitable area. Projected responses to climate change differed markedly among species. Suitable habitat for O. coriaceus decreased from 60,312 km² to 41,387 km² (− 31.4%), while O. hermsi increased from 25,194 km² to 30,390 km² (+ 20.6%), and O. parkeri expanded from 34,625 km² to 46,258 km² (+ 33.6%). Environmental drivers varied across species but were consistently dominated by temperature- and precipitation-related variables, alongside elevation, highlighting distinct ecological niches and sensitivities to climatic gradients. Integration of MaxEnt and RF binary outputs allowed identification of consensus areas of suitability, reducing model-specific uncertainty and improving robustness of spatial predictions. County-level summaries revealed substantial heterogeneity in both current and future distributions, with northern and montane regions showing the greatest increases in suitability for O. hermsi and O. parkeri , while contractions of O. coriaceus were concentrated in lower-elevation areas.These results demonstrate that climate change is likely to reshape the spatial distribution of Ornithodoros species in divergent ways, with important implications for vector-borne disease risk. Species-specific modeling frameworks such as this provide a critical foundation for targeted surveillance and adaptive management strategies in California.

Editorial Expression of Concern: Study on mechanical properties and energy change of rock materials in whole splitting process based on peridynamics

An efficient hybrid quantum machine learning framework for Alzheimer classification

Abstract Neuro-imaging data is necessary for timely and correct diagnosis of the disease Alzheimer’s (AD). Early clinical intervention is challenging due to the high-dimensional nature of the images, making it computationally difficult to work on representations, sparse annotated datasets and inter-subject variance. Deep convolutional VGG, ResNet, DenseNet, MobileNet and other neural networks (CNNs) have shown good performance. Diagnostic performance, but they are based on a high number of parameters and require highly trained personnel. Data can often times restrict resilience to data-skimy clinical settings. To address these challenges, the current paper presents a hybrid Quantum Machine Learning (QML) architecture for Alzheimer’s disease Detection and classification based on a combination of classical CNN-based feature extraction and Variational quantum classifier. In the suggested method, diseased, relevant, and compact features are extracted using pre-trained CNN backbones. Data from structural MRI scans is later translated into quantum states and computed. With parameterised variational quantum circuits optimised with a hybrid quantum-classical learning loop. The proposed method has been experimentally evaluated on benchmark datasets of MRI scans of Alzheimer’s patients. The hybrid CNNQML model has a mean classification accuracy of about 93% which is similar to deep CNN models, including ResNet and DenseNet (94%), and much more. Beating lightweight models like MobileNet and shallow CNNs by six percentage points. It is worth noting that the hybrid QML model performs better in conditions with limited training data. Performance degradation is observed in accuracy and AUC for all CNN variants, with up to 5% lower performance to deep CNNs. Cross-validation, confidence interval, paired significance test, and statistical validation. Effect size analysis proves the fact that the hybrid model produces statistically significant performance, as well as cases of clinically meaningful improvements over lightweight CNN baselines. Similar to the state-of-the-art deep networks with significantly reduced trainable parameters in the classification stage. These findings show that hybrid QML is a data-efficient and powerful substitute in the diagnosis of Alzheimer’s. On the whole, this is a work that sets a practical way to integrate quantum machine learning into clinical neuro-imaging pipelines and hybrid settings. QML has supplementary deep-learning enhancement to classics in next-generation Alzheimer’s Decision-support systems.

Evaluation of different defect-inspection setups for injection molding parts based on the deep learning method

Abstract In the injection molding industry, the shift toward small-batch production has led to a greater variety of products and smaller batch sizes, necessitating frequent mold changes and efficient quality control, which still largely relies on human operators. This study proposes a comprehensive methodology for evaluating and comparing deep learning-based automatic optical inspection (AOI) strategies to detect complex surface defects in injection-molded parts. Three inspection setups were assessed: static frontal imaging, belt conveyor inspection, and robotic-assisted inspection. The findings reveal clear differences in defect detection capabilities among the methods, with the robotic-assisted approach demonstrating superior performance, achieving higher defect detection accuracy due to its flexibility in optimizing camera angles and positions. The proposed methodology serves as a workflow to systematically evaluate and optimize inspection setups across different parameters, enabling informed decisions about AOI systems design. This research contributes to narrowing the gap between the development of advanced detection algorithms and their industrial application, offering insights into the strategic implementation of AI technologies in quality control processes and enhancing the automatic detection of challenging defects.

Task-moment mismatch: diagnosing failures of coverage-based methods on heavy-tailed distributions in coreset selection

Abstract Coreset construction aims to identify a small, weighted subset of training data that approximates the full dataset’s statistical properties, thereby enabling efficient model training without significant performance loss. Existing coreset selection methods typically optimize for geometric coverage or mean matching, under the implicit assumption that these objectives transfer meaningfully to arbitrary downstream tasks. In this study, it is demonstrated that this assumption can fail when the downstream task depends on statistical moments that the selection objective often does not preserve. A common underlying phenomenon termed the task-moment mismatch is identified, wherein coreset objectives that neglect task-relevant statistical moments yield worse performance than uniform random sampling. The objective of this work is threefold: (1) to diagnose failure modes of coverage-based coreset selection on heavy-tailed distributions, (2) to characterize how optimizing for lower-order moments can degrade the preservation of higher-order statistical structure required by certain tasks, and (3) to provide a constructive demonstration that moment-aware selection can address this mismatch. A greedy selection procedure termed Hierarchical Moment-Preserving (HMP) selection is proposed, which targets specific moment tensors through a two-stage hierarchical approach. Experimental evaluation across synthetic distributions and realistic signals parameterized from real-world sources reveals that k-means++ exhibits $$5\times$$ higher covariance error than random sampling on heavy-tailed data, confirming that coverage optimization can be counterproductive for moment-dependent tasks. Furthermore, covariance-optimized coresets are shown to cause $$-13.3$$  dB signal separation degradation on Independent Component Analysis tasks ( $$p = 0.002$$ ), attributable to the disruption of fourth-order cumulant structure that ICA requires. The proposed moment-aware approach achieves $$3\times$$ lower covariance error than random sampling and $$+15\%$$ improvement in outlier detection precision on financial data. A practical decision matrix is provided to guide practitioners in matching coreset methods to the statistical requirements of their downstream tasks.

Novel gas tungsten powder filler metal arc welding process

Abstract In the present investigation, an innovative and patented welding process referred to as gas tungsten powder filler metal arc welding (GTPFAW) has been developed, utilizing a micro powder mixture as a filler metal in lieu of conventional welding wires. This approach presents numerous advantages, encompassing a refined welding microstructure, enhanced mechanical properties, adaptability in composition tailored for various welding applications, and the elimination of the necessity for utilizing prefabricated welding wires. Steel grade S275 JR was employed as the foundational metal to be joined with the powder filler metal, which possessed a composition corresponding to that of the standard solid wire ER 70-S3. The evaluation of the weldability of gas tungsten powder filler metal arc welding (GTPFAW) methodology was conducted through a comprehensive analysis of the overall joint integrity and its corresponding mechanical attributes. The findings indicated that GTPFAW exhibited user-friendliness and yielded effective welding outcomes. The joint properties that resulted from the novel approach of GTPFAW were methodically contrasted with the joint properties generated by the traditional gas tungsten arc welding (GTAW) technique in combination with solid wire ER 70-S3, ensuring equivalent joint dimensions and the same steel parent metal classification S275 JR. The microstructural examination disclosed a significant acicular ferrite morphology characterized by its ultra-fine nature, markedly finer than the conventional microstructure produced by the solid filler. Furthermore, the weld metal produced exhibited a consistent hardness range (135–143 HV5) throughout its entire cross-section. This innovative technique possesses the capacity to enable a broad spectrum for the enhancement of filler metal formulations, thus providing substantial opportunities for the union of metals that display or possess particular or distinctive compositions. Moreover, this novel welding approach may be regarded as an environmentally sustainable practice and a proponent of the 2030 Agenda for Sustainable Development.

Closing the sepsis gap: from molecular mechanisms to scalable bedside care

Abstract Recent estimates point to roughly 49 million cases and 11–13 million deaths annually due to sepsis, with a disproportionate burden borne by low- and middle-income countries. Three decades of progress, summarised in the recently updated Surviving Sepsis Campaign guidelines, have refined how clinicians screen for, resuscitate, and treat patients with sepsis. Yet, persistent uncertainty surrounds early identification, pathogen detection, hemodynamic targets, adjunctive therapies, and the long-term burden carried by survivors. This Collection, Sepsis: Treatment, intervention, mortality, brings together original research that maps where the field is moving: pragmatic diagnostics that run on standard hospital equipment; multi-omic biomarker discovery in blood, plasma extracellular vesicles, and urine; computational dissection of pathogen biology and longitudinal host-response trajectories; preclinical interrogation of metabolic and signalling pathways implicated in organ injury; and mechanistic studies of the muscle and mitochondrial wasting that shape life after sepsis. Together, these papers sketch a research agenda for sepsis that is simultaneously molecularly precise and operationally scalable.