Browse Articles

Overcoming pitfalls in multi-stack diffusion MRI for tractography reconstruction of skeletal muscles

Abstract Accurate in vivo characterization of skeletal muscle structure is essential for understanding muscle function, assessing pathologies, and developing musculoskeletal models. Magnetic resonance imaging (MRI) and diffusion MRI enable visualization of muscle morphology and fibre architecture in vivo, but imaging skeletal muscles often requires an extended field of view. At large distances from the magnet isocentre, gradient non-linearities and magnetic field inhomogeneities can cause severe image distortions. Here, we present multi-stack structural and diffusion-tensor echo-planar images of the lower limb using selected stack lengths and acquisition parameters to provide realistic examples. Geometric distortions, fractional anisotropy maps, and tractography-reconstructed fibre tracts are used to demonstrate anatomical plausibility and consistency across overlapping slices. In the presented examples, pronounced distortions, compromised fat suppression, and fibre reconstruction errors are observed under off-isocentre imaging conditions. While shorter stack lengths may help reduce distortions, our examples also indicate that factors such as shimming strategy, participant positioning, and post-processing can influence image quality. Our work highlights key pitfalls in muscle diffusion MRI, providing practical guidance grounded in MRI physics to support the design of multi-stack MRI protocols. These considerations support more reliable multi-stack diffusion MRI for musculoskeletal research and biomechanical modelling.

Chemical‐Sensing Catheters for Long‐Term and Personalized Therapeutic Management

ABSTRACT Long‐term intravenous catheterization is vital for delivering chemotherapy to cancer patients and parenteral nutrition to those with intestinal failure, but it inevitably leads to complications, including metabolic disorders, bloodstream infection, liver injury, and kidney injury. Clinical monitoring methods show notable delays, while real‐time blood monitoring devices fail to ensure long‐term stability in vivo. Here, we present a chemical‐sensing catheter that integrates multiple analyte sensors, including creatinine, taurocholic acid, bilirubin, and taurine, for real‐time monitoring of complications in patients receiving long‐term chemotherapy or parenteral nutrition in situ. The design of the metalgel preserves signal integrity through 1 million bending deformations, and a dual‐network molecularly imprinted polymer suppresses in vivo performance degradation, enabling stable long‐term operation of the chemical‐sensing catheter over a 3 month implantation period. In chemotherapy and parenteral nutrition applications, early monitoring of liver and kidney injury, combined with prognostic intervention, mitigates risks such as bile stasis, hepatitis, and hepatic steatosis. With the integration of machine learning analysis of multimodal sensor data, this technology provides early prediction of complications and establishes a new paradigm for personalized therapeutic management.

Cortical and Thalamic Afferent Connectomes Distinguish ACC Subregions of the Macaque Brain

In humans and nonhuman primates, the anterior cingulate cortex (ACC) is an interface between “interoceptive” and “exteroceptive” domains. The ACC contains discrete subdivisions that are distinct in cytoarchitecture, connectivity, and function. The subgenual ACC (sgACC) is a key area for arousal state modulation. Importantly, the sgACC is dysregulated in major depression and a target for neuromodulation therapies, including deep brain stimulation. In contrast, the perigenual ACC (pgACC) is important for cognitive functions, including social decision-making. Understanding the major sources of afferent input to the sgACC and pgACC is essential for elucidating functional modulation, including in major depression. We took a mesoscopic, “connectomic” approach to examine the balance of projections to ACC subdivisions from two sources of glutamatergic input: the prefrontal cortex (PFC) and insula and the thalamus ( n  = 6 macaques). Using retrograde tracer injections into ACC subdivisions in male macaques, and unbiased statistical clustering, we revealed that the ACC subdivisions are under the influence of strikingly different “heavily weighted” (HW) inputs from PFC and thalamus. Only one cortical region, area 10m, has HW projections to both sgACC and pgACC, suggesting an integrative role. Additionally, the pgACC sends “top-down” inputs to the sgACC, without significant “bottom-up” return input. Finally, ACC and thalamus-ACC circuits are hierarchically organized, governed by cortical granularity and thalamo-cortical connectivity. Agranular cortices and their associated thalamic nuclei formed most inputs to the sgACC. In contrast, pgACC receives a balanced set of afferents from agranular, dysgranular, and granular cortices, coupled with inputs from broader thalamic regions associated with these cortices.

Exploring imputation performance of phenotype-associated SNPs for forensic prediction models

Abstract Imputing single nucleotide polymorphisms (SNPs) to expand genotyped SNP panels is commonly applied to study genotype-phenotype correlations in medical and population genetics but is largely unexplored in forensics. Forensic DNA phenotyping, i.e., the SNP-based phenotype prediction, can greatly benefit from imputing missing prediction model SNPs. Unfortunately, most imputation studies investigate the performance of random SNPs with limited focus on phenotype-associated SNPs. Here, we compare the imputation accuracy of SNPs leveraged in phenotype prediction models, phenotype-associated SNPs and all SNPs and explore the performance of phenotype prediction model, HIrisPlex-S, using imputed datasets. We confirm that the number and selection of SNPs in the genotype dataset and minor allele frequencies (MAFs) are major drivers of imputation call and error rates. Phenotypic SNPs show higher imputation error rates than randomly selected SNPs due to MAF differences. Further, we provide novel insights into accurate phenotype prediction for dense SNP panels of high linkage, frequent phenotypes in the reference panel, and lenient imputation thresholds due to the detrimental effect of missing genotypes on trait prediction compared to imputation errors. Overall, we show the importance of imputation performance studies for SNPs of interest and the high applicability of SNP imputations prior to phenotype prediction.

Unveiling α‐MoO ₃ as a High‐Index, Low‐Loss Anisotropic Material: Bridging the Gap From Mid‐Infrared to Ultraviolet Nanophotonics

ABSTRACT Modern nanophotonics demands light manipulation at a subwavelength scale, a capability historically constrained by the isotropic dielectric response of conventional optical semiconductors. Here, we establish α‐MoO 3 as a high‐index, low‐loss anisotropic material that bridges the gap between mid‐infrared polaritonics and short‐wavelength nanophotonics. Integrating spectroscopic ellipsometry, first‐principles calculations, and near‐field optical microscopy, we determine its broadband 250–1700 nm anisotropic optical constants and combine them with a phonon model in mid‐infrared to 30 µm. While the mid‐infrared phonon polariton properties of α‐MoO 3 are well established in the literature, the present work provides the first experimental characterization of its complete anisotropic dielectric tensor in the ultraviolet‐to‐near‐infrared regime, conceptually bridging these two spectral domains in which this material finds application. The pronounced out‐of‐plane birefringence 0.35 suppresses evanescent crosstalk beyond competing van der Waals (vdW) waveguide materials, and the high in‐plane anisotropy enables zero‐order waveplates at wavelengths as short as 325 nm. As a practical demonstration, we prepared nanowaveguide which exhibits wavelength‐selective light guidance at visible range. Together, these results reposition α‐MoO 3 from a mid‐infrared specific material to a versatile wide‐bandgap platform bridging mid‐infrared polaritonics and ultraviolet‐visible nanophotonics.

Association between albumin corrected anion gap and persistent acute kidney injury in patients with sepsis-associated acute kidney injury: a retrospective study based on the MIMIC-IV database

Synergistic Ni–Co Dual Single Atoms on Oxygen‐Deficient CeO ₂ Nanorods for Efficient and Coke‐Resistant Photothermal Dry Reforming of Methane

ABSTRACT Solar‐driven photothermal catalytic dry reforming of methane (DRM) offers a sustainable route to convert two greenhouse gases into syngas, yet its practical application is limited by unsatisfactory performance and deactivation. Here, we report an efficient and stable photothermal DRM catalyst based on synergistic Ni‐Co dual single‐atom catalysts anchored on CeO 2 nanorods. Under 12.17 W cm −2 illumination, the optimized Ni 8 Co 4 /CeO 2 delivers high H 2 and CO production rates of 3383.01 and 4703.36 mmol g −1 h −1 , respectively, with an H 2 to CO ratio of 0.72 that exceeds the thermodynamic equilibrium limit. Moreover, a record‐high light‐to‐fuel efficiency of 40.9% is obtained with excellent long‐term stability. Mechanistic studies reveal a cooperative dual single‐atom effect. Ni single atoms act as the primary sites for CH 4 activation, while Co single atoms stabilize abundant oxygen vacancies in the CeO 2 support, promoting CO 2 activation to form O* and OH* species. These oxygen fluxes intercept CH 3 * via the oxygen‐assisted CH 3 O* pathway, reducing carbon deposition and the side reaction. These findings present a general strategy that employs dual single‐atom engineering to develop efficient, coke‐resistant catalysts for solar‐driven highly endothermic reactions.

Interference-aware optimization of three-tier RIS-enhanced hierarchical aerial computing: integrating terrestrial base stations for persistent 6G IoT coverage

Abstract The proliferation of Internet of Things (IoT) devices in emerging 6G networks demands computing architectures that simultaneously deliver high throughput, low latency, and persistent coverage across heterogeneous deployment environments. Existing two-tier unmanned aerial vehicle–high-altitude platform (UAV–HAP) frameworks offer flexible edge processing but suffer from limited battery endurance, constrained computational capacity, and susceptibility to co-channel interference (CCI) when multiple aerial platforms share the same spectrum. This paper proposes a novel three-tier RIS-enhanced hierarchical aerial computing architecture that integrates a grid-powered reconfigurable intelligent surface–equipped base station (BS-RIS) alongside four RIS-equipped UAVs and a stratospheric HAP, so as to provide persistent, interference-managed 6G IoT coverage. The proposed architecture introduces a sub-array RIS partitioning mechanism in which each RIS panel, consisting of 256 elements divided into 4 sub-arrays, dedicates one sub-array per neighboring interfering platform, achieving 85 % inter-platform interference suppression (residual fraction $$\psi ^{\textrm{sup}}=0.15$$ ). A comprehensive signal-to-interference-plus-noise ratio model is derived that captures both intra-platform CCI and inter-platform interference across all tiers. The resulting joint mixed-integer nonlinear programming problem is decomposed into three sequential stages: (i) a three-way hotspot-aware stable matching algorithm that associates IoT devices to platforms while penalising interference-heavy assignments; (ii) a sub-array-aware Riemannian conjugate gradient phase optimization that simultaneously enhances desired signal gains and suppresses inter-platform leakage; and (iii) a platform-aware hierarchical task distribution algorithm applying differentiated local-processing thresholds for battery-constrained UAVs (70 % delay margin) versus the grid-powered BS (100 % threshold). Extensive Monte Carlo simulations demonstrate that the proposed framework achieves approximately $$30\,\%$$ higher total computed data volume, $$15\,\%$$ points higher task completion rate, and $$20\,\%$$ lower average end-to-end delay compared to the two-tier UAV–HAP.

From Lab to Fab: Path Forward for Multi‐Scale Design of Industrial Anion Exchange Membrane Electrolyzers

ABSTRACT Anion exchange membrane water electrolysis (AEMWE) has emerged as a pivotal pathway bridging laboratory‐scale research to large‐scale hydrogen production. Nevertheless, its commercialization is constrained by multi‐scale failure mechanisms that operate across scales ranging from atoms to entire systems, including catalyst dissolution and reconstruction, degradation of the three‐phase interface with membrane electrode assemblies, corrosion of bipolar plates, and uneven stack assembly. This review systematically investigates the failure behaviors of AEM electrolyzers across multiple scales and introduces a collaborative design strategy spanning from the atomic to the system level. By integrating innovative membrane‐electrode architectures, biomimetic flow‐field designs, and advanced intelligent control systems, we establish a full‐chain optimization scheme spanning materials, devices, and systems that simultaneously improves current density, durability, and dynamic response. Emphasizing the critical roles of in situ characterization and artificial intelligence in elucidating failure mechanisms and enabling predictive control. This review also provides a systematic multi‐scale design blueprint and a technical pathway for transitioning AEMWE from laboratory‐scale prototypes (“Lab”) to gigawatt‐scale fabrication facilities (“Fab”). Ultimately, it aims to facilitate the adoption of AEMWE as an efficient and reliable industrial solution for the green hydrogen economy.

Chemical and evolutionary reasons for the rarity of natural products containing nitrogen–nitrogen bonds (NN bonds) in life’s biochemical repertoire

Abstract Understanding the chemistry of life is a central motivation of biological research. This includes understanding why life does not use particular chemical structures. Compounds containing a bond between two nitrogen atoms (NN bonds) are relatively rare in the chemicals made by life. We quantify that rarity, and explore possible reasons for it. We find that the main limitation on the use of NN compounds in biochemistry could be the toxicity of NN bond containing metabolic intermediates in their biosynthesis. The potential toxicity of the final NN compound metabolites, the energy required for their synthesis, and differences in the functional benefit from their synthesis may play additional, minor roles in explaining the NN bond rarity in biochemistry. Our study adds to a growing body of work that aims to map and explain the constraints behind life choices in chemical space.

Electrostatic Gaussian Transistor for Real‐Time Probabilistic Inference

ABSTRACT Gaussian distribution functions underpin a wide range of probabilistic computing models, yet their faithful and tunable implementation at the hardware level remains a fundamental challenge. Conventional approaches based on anti‐ambipolar transistors rely on heterojunctions formed from dissimilar semiconducting materials, introducing intrinsic asymmetries in carrier mobility, interface quality, and band alignment that prevent accurate mirroring of symmetric Gaussian curves. Here we report a single‐material, single‐channel split‐gate Gaussian‐mirroring transistor (SC‐GMT) that generates symmetric, Gaussian‐shaped transfer curves through reversal voltage biasing. By independently modulating carrier concentrations via split‐gate control, the device achieves tunable amplitude, mean, and standard deviation with >99.99% coefficient of determination to ideal Gaussian distributions. To demonstrate practical utility, we integrate the SC‐GMT into a custom‐built printed circuit board with digital‐to‐analog control and real‐time current sensing. Using this platform, we implement a hardware Gaussian Naive Bayes (GNB) classifier capable of distinguishing deepfake and authentic voices with 82% accuracy. Moreover, the transistor's drain current scales with the product of two gate voltages, enabling quadratic‐order analog multiplication critical for probabilistic models and attention‐based architectures.

Synergistic Upcycling of NH ₃ and CO ₂ Gases via an Integrated Tandem Photocatalytic System

Cross-platform software vulnerability detection using Vulnerascope-X with Word2Vec Node2Vec and RCGO optimized SVM

Abstract To increase program dependability and reduce maintenance expenses, software defect prediction is essential. For software vulnerability study across platforms, this paper presents a state-of-the-art methodology that makes use of a unique synthetic dataset called VulneraScope-X. Utilising synthetic CVE intelligence, this dataset incorporates a wealth of static, syntactic, and semantic information. This work employs an array of pre-processing methods, such as min-max normalisation, Word2Vec, Node2Vec, Synthetic Minority Oversampling Technique (SMOTE) for class balance, to deal with complexity and diversity of the features. The recently suggested Running City Game Optimiser (RCGO) outperformed state-of-the-art metaheuristics in feature selection while simultaneously lowering dimensionality and keeping predictive characteristics. With help of the features that were chosen, a Support Vector Machine (SVM) classifier was trained. The hyperparameters of this classifier were adjusted using Grid Search. Outperforming more conventional classifiers like Naive Bayes, MLP, and KNN, the model produced remarkable results with a 94.25% accuracy rate, 93.90% precision rate, 94.10% recall rate, and AUC-ROC of 0.962. In terms of accuracy and execution time, the RCGO algorithm outperformed other optimisation algorithms such as GA, GWO, CRO, and BWO. This scheme provides reproducible and scalable methodologies for software security evaluation in addition to demonstrating a high-performing pipeline for defect prediction. According to the findings, VulneraScope-X greatly improve cross-platform defect detection when combined with topological and semantic embeddings. When applied to large-scale, heterogeneous software organizations, this method demonstrates promise for vulnerability triaging and safe software development.

Photoactivable Surfactant Design for In Situ Stabilization of Hydrogel Bioelectronics

ABSTRACT Conductive polymer hydrogels offer unique advantages for soft, stretchable biointerfaces by combining tissue‐like mechanics with high ionic conductivity. However, their reliable integration with hydrophobic substrates and encapsulants essential for electrical insulation and chemical protection remains a major challenge due to poor wetting and interfacial delamination in aqueous environments. Here, we report a facile strategy for the in situ stabilization of conductive polymer hydrogels on hydrophobic substrates using a photoactivable surfactant (PAS). PAS reduces interfacial surface energy and, upon UV activation, forms covalent bonds with stretchable substrates, thereby yielding strong adhesion and long‐term stability under wet conditions. This molecular design also enables photopatterning of hydrogels without compromising performance. Notably, PAS not only stabilizes and patterns conductive hydrogels in situ but also enhances their mechanical and electrical properties. PAS‐integrated hydrogel biointerfaces exhibit robust operation in aqueous environments and demonstrate reliable in vivo electromyographic (EMG) signal recording, underscoring the potential of this approach for next‐generation implantable and wearable bioelectronics.

Retinal vessel segmentation using Kolmogorov-Arnold networks and test-time adaptation

A Super‐Bright and Temperature‐Inert Au Complex Scintillator With Thermally Stimulated Delayed Phosphorescence Characteristic

ABSTRACT Scintillators serve as crucial core materials in high‐energy radiation sensors. Molecule‐based scintillators, one of the important kind of scintillators, have demonstrated outstanding advantages, such as high water‐oxygen stability, low cost, and low toxicity. However, limited exciton utilization and luminescence efficiency have an impact on the radioluminescence properties of materials, severely limiting the development of molecule‐based scintillators. In this work, the first thermally stimulated delayed phosphorescence (TSDP) molecule‐based scintillator ( Au(TFPP) 3 ) was reported. Spin‐allowed reverse internal conversion (RIC) of triplet excitons occurs in the TSDP process, leading to efficient utilization of thermally sensitive triplet excitons and overcoming the significant temperature effect of scintillators. As a result, Au(TFPP) 3 not only has an ultrahigh photoluminescence quantum yield (PLQY) exceeding 99% but also has an excellent light yield (LY) as high as 99475 ± 275 photons MeV −1 . More importantly, all these luminescence properties are temperature independent over a very wide range from 100 to 370 K. The super‐bright and temperature‐inert characteristics render Au(TFPP) 3 highly suitable for ionising radiation detection, and Au(TFPP) 3 has been effectively applied in low‐dose x‐ray variable‐temperature imaging and α/β particle detection.

Trans-dimerization of Amyloid Precursor Protein Family Members Induces Pre- and Postsynaptic Differentiation through Distinct Signaling Pathways

The amyloid precursor protein (APP), a key factor in Alzheimer's disease (AD) pathology, and its two mammalian homologs, amyloid precursor-like proteins 1 and 2 (APLP1 and APLP2), are considered as members of the synaptic adhesion molecule (SAM) family. They are localized to the pre- and postsynapse, form trans-cellular dimers, and have been shown to induce presynaptic differentiation in a heterologous synapse formation assay. We demonstrate that expression of all APP family members in non-neuronal cells also promotes dendritic excitatory postsynaptic differentiation in primary mouse neurons of either sex, similar to Neurexin1β and other SAMs. Synaptogenic activity was decreased by deletion of the E1 domain and increased upon inhibition of soluble APP (sAPP) generation, reinforcing that trans-cellular interaction of APP/APLPs can induce synaptogenesis. Consistent with this, the capacity of heterologously expressed APP to induce postsynaptic specializations in contacting dendrites was reduced by the absence of APP family members at the postsynaptic site and was lost in conditional triple knock-out (cTKO) neurons. Pharmacological analyses revealed that heterologous formation of pre- and postsynapses relies on proper microtubule and actin cytoskeleton dynamics, as well as the MAP kinase pathway, similar to what has been shown for Neurexin1β and Neuroligin1. However, inhibition of the PI3K/Akt pathway selectively impaired APP-induced postsynaptic differentiation, suggesting that distinct APP signaling pathways are required for pre- and postsynaptic differentiation. Collectively, our data highlight the role of all APP family members as SAMs in trans-synaptic signaling, providing key insights into their physiological function and advancing our understanding of AD-related synaptopathies.

Neural adaptation in Dyslexia: differential patterns for orthography and phonology

Abstract Dysfunction in neural adaptation to words might be at the core of reading impairment. Children with dyslexia can present difficulties recognizing similarities between repeated linguistic stimuli, as evidenced by a lack of reduced activations (adaptation) in brain regions associated with reading. fMRI Rapid Adaptation (fMRI-RA) provides a sensitive index of the quality of orthographic and phonological representations to some degree independent of the access to those representations. We used fMRI-RA to compare neural adaptation in children (10.01–14.16 y.o.) with dyslexia (DYS; n = 32) and typically reading controls (CON; n = 40). Phonological adaptation was measured in the left temporoparietal cortex (TPC) and orthographic adaptation in the left anterior ventral occipitotemporal cortex (VOT). In TPC, the Group × Condition interaction reached trend level. Planned contrasts, conducted based on a priori hypotheses, were nevertheless consistent with greater phonological selectivity in CON than DYS. Both groups showed comparable orthographic adaptation in VOT. These findings suggest a preserved neural adaptation effect for short, easy, high-frequency words in the VOT in dyslexia group. In sum, reading difficulties may reflect not only single modality deficit but also variations in how orthographic and phonological representations are integrated, highlighting the importance of approaches that examine the interaction between these processes.

Metal‐Ligand Coordination Enables Molecular Resist‐Based Direct Write Lithography of Metal Halides

ABSTRACT Lithographic patterning of semiconductor materials is essential for most modern optoelectronic devices. However, traditional inorganic and nanocrystal derived resists exhibit low electron‐dose sensitivity, weak solubility contrast, and limited chemical compatibility, restricting high resolution functional lithography. Here, we present a molecular complex platform that converts ordinary metal halides (MXn; nine compositions) into intrinsically electron beam responsive, solution processable resists for direct write electron beam lithography. Coordination of MX n with oleylamine yields metal‐ligand complexes with comparatively low dose sensitivity among additive‐free inorganic resists (0.81 mC cm −2 ), high contrast (γ = 3.1), and sub‐30 nm resolution. Across the tested metal halide library, resist sensitivity shows an exponential dependence on molecular weight, establishing the first universal scaling relationship for molecular resist energetics. Mechanistic studies reveal that electron irradiation induces bond cleavage and coordination network collapse, generating metal halide domains with high structural fidelity. The patterned nanostructures retain optical functionality, nanodots displaying super linear PL excitation (α > 1) characteristics. Furthermore, sequential multilayer writing enables deterministic RGB nano‐pixel architectures, exemplified by registered 3.9 × 10 4  pixel full color parrot micrograph. This additive free, tunable molecular resist system provides a high‐resolution lithography route for scalable quantum photonic and optoelectronic fabrication.

Quantifying Changes in Pain Sensitivity Using Reproducible Transcutaneous Optogenetic Stimulation in Behaving Mice

Optogenetics provides an unprecedented opportunity to delineate how different somatosensory afferents contribute to sensation, including pain. Afferents expressing channelrhodopsin-2 (ChR2) can be selectively activated by transcutaneous photostimuli applied to behaving mice. Despite targeting expression of ChR2 to specific cell types, imprecise photostimulation has hindered quantitative optogenetic-based behavioral testing. Here, we used a robot to reproducibly apply transcutaneous optogenetic stimuli to the hindpaw of mice while measuring nocifensive withdrawal. Different photostimulus waveforms (pulses and ramps) and response metrics (threshold and latency) were compared in mice of either sex expressing ChR2 in all afferents (Advillin-ChR2) or preferentially in nociceptors (Na V 1.8-ChR2). Inflammation induced by complete Freund’s adjuvant (CFA) caused withdrawal from ramped photostimuli to become faster relative to baseline and to vehicle-injected controls whereas pulsed photostimuli revealed a modest increase in threshold. Analgesia caused by Na V 1.7 and 1.8 channel blockade was evident in both testing protocols. Overall, ramp-based testing was more effective and more efficient (i.e., required less time and total stimulation) than pulse-based testing. Electrophysiological measurements confirmed that CFA increases nociceptor excitability without affecting phototransduction, suggesting that withdrawal latency depends on the number of nociceptors activated rather than how strongly each nociceptor is activated. Consistent with changes described in nociceptor somata, the behavioral consequences of peripherally blocking different voltage-gated sodium (Na V ) channels showed that nociceptor axons normally rely on Na V 1.8 but upregulate Na V 1.7 after CFA, with important clinical implications for drug efficacy. Collectively, these results demonstrate the utility of optogenetic pain testing when reproducibly delivered and strategically designed photostimuli are used.