Browse Articles

SHI: a framework for spatial harmonic imaging

Abstract Despite the growing interest in multicontrast X-ray imaging, spatial harmonic imaging remains limited by a lack of specialized computational resources. In this paper, we present SHI , a high-performance software framework that covers the range from data acquisition to processing in spatial harmonic imaging experiments. SHI is an open-source software package that facilitates the acquisition of precise measurement data and streamlines the workflow, ensuring that data can be efficiently organized, processed, and visualized, leading to high quality results. In addition, SHI includes higher-order harmonic extraction. Preliminary results show that spatial harmonic imaging improves experimental robustness and retrieves refraction and scattering information, albeit with reduced resolution. However, using these lower-resolution images enables faster CT reconstruction with fewer projections, while preserving essential sample features and allowing a substantial reduction in exposure levels. This research focuses on the acquisition methodology and the subsequent data processing for contrast retrieval and multicontrast computed tomography.

Refractory eosinophilic duodenal bulb ulcer associated with Helicobacter pylori eradication in children: a multicenter study

Quantum spin resonance in engineered proteins for multimodal sensing

Abstract Sensing technologies that exploit quantum phenomena for measurement are finding increasing applications across materials, physical and biological sciences 1–7 . Until recently, biological candidates for quantum sensors were limited to in vitro systems, had poor sensitivity and were prone to light-induced degradation. These limitations impeded practical biotechnological applications, and high-throughput study that would facilitate their engineering and optimization. We recently developed a class of magneto-sensitive fluorescent proteins including MagLOV, which overcomes many of these challenges 8 . Here we show that through directed evolution, it is possible to engineer these proteins to alter the properties of their response to magnetic fields and radio frequencies. We find that MagLOV exhibits optically detected magnetic resonance in living bacterial cells at room temperature, at sufficiently high signal-to-noise for single-cell detection. These effects are explained through the radical-pair mechanism, which involves the protein backbone and a bound flavin cofactor. Using optically detected magnetic resonance and fluorescence magnetic-field effects, we explore a range of applications, including spatial localization of fluorescence signals using gradient fields (that is, magnetic resonance imaging using a genetically encoded probe), sensing of the molecular microenvironment, multiplexing of bio-imaging and lock-in detection, mitigating typical biological imaging challenges such as light scattering and autofluorescence. Taken together, our results represent a suite of sensing modalities for engineered biological systems, based on and designed around understanding the quantum-mechanical properties of magneto-sensitive fluorescent proteins.

Orientation driven design and mechanical optimization of gyroid TPMS lattice structures

Abstract Triply Periodic Minimal Surface (TPMS) lattice structures, particularly Gyroid morphologies, are gaining attention for their high specific strength, energy absorption, and geometric adaptability. However, the large body of prior studies has focused on comparing different TPMS topologies or materials, with limited attention to how the build orientation of a single Gyroid structure influences its mechanical behavior. Addressing this gap, this study presents a novel design strategy by systematically varying the build orientation of Gyroid structures fabricated via Fused Filament Fabrication (FFF) using PLA-metal composite. Six models, termed G0 - G5, were created by altering orientation angles relative to the z-axis. Experimental and finite element analyses showed strong agreement and revealed that axially aligned models (G1, G3, G5) achieved significantly higher stiffness, strength, and energy absorption. Homogenization confirmed orientation-dependent anisotropy, and true stress analysis based on CAD-derived cross-sections improved stress accuracy. Increasing wall thickness induced a shift from bending- to stretch- and shear-dominated deformation. Power-law fitting of mechanical properties versus relative density achieved R² > 0.95, validating Gibson-Ashby scaling. These insights support tailored Gyroid designs for crashworthiness, aerospace, automotive and biomedical applications.

Designing an eco-friendly graphene oxide-based titanium tetra-acetoximate modified epoxy coating for anti-corrosion properties of aluminium

Thalamocortical transcriptional gates coordinate memory stabilization

Evidence of a genomic basis for growth rate variation in a natural kelp population

Abstract Understanding the genetic architecture of functional traits can provide key insights into the ecological dynamics and adaptive potential of species. We investigated whether genetic data can predict growth rate variation in a natural population of the widespread kelp, Ecklonia radiata . We tagged kelps and tracked their growth in situ over spring when growth is maximal. Individual kelps were then genotyped using reduced representation sequencing (ddRAD) and we employed multiple approaches to assess whether genetic variation corresponded with growth rate variation. Despite a limited sample size, we found evidence that growth rate can be strongly predicted from genetic variation, with approximately half of the variation in growth rate predicted by only 18 loci (R 2  = 0.499). Leveraging published transcriptomic data, we confirm that most of these loci are expressed or are linked to expressed putative genes. However, many of these genes are of unknown function and do not match well-known gene families. These findings have important implications for understanding natural kelp forest dynamics and for applied approaches such as selective breeding and aquaculture. While our study offers an important first assessment of the possible genomic architecture underlying growth rate in E. radiata , future work is needed to confirm this apparent link between genetic and functional variation.

An intelligent single valued neutrosophic MCDM framework for Business English language analysis curriculum planning and pedagogical support under uncertainty

A multi-source physiological data-driven method for assessing the hazard perception level of operators

Mapping at-risk transportation infrastructure assets using statistical and machine learning methods

Abstract Geotechnical assets such as highway embankments and slopes (HWS) are critical to the integrity of transportation infrastructure. However, they are largely overlooked by Transportation Asset Management programs in the United States. The HWS are vulnerable to landslides induced by several factors including frequent occurrences of extreme rainfall events. Therefore, mapping vulnerable HWS and developing an inventory will significantly help with infrastructure asset management. To this end, this research adopted proven geographical information systems based on landslide susceptibility mapping methods typically applied to hillside slopes, and a method for mapping at-risk HWS assets was developed. Several supervised machine learning (ML) classification models were developed and evaluated to accurately map at-risk HWS in the study area of central Mississippi. Digital Elevation Models (DEMs) created from remote sensing data obtained from satellites, drone sensors, and terrestrial LiDAR were utilized to develop rasterized causative factors. The causative factors used included: Geotechnical and geomorphological attributes, such as slope, aspect, curvature, elevation, normalized difference vegetation index (NDVI), soil composition, and terrain from DEM; and hydrological factors, including precipitation, distance from the stream, groundwater depth, and topographic wetness index. Known locations of failed and not-failed HWS were selected and rasterized, and the pixels were extracted as ground truth data. The rasterized causative factors were utilized as independent features to train the classification ML models for predicting HWS failure susceptibility. Models were evaluated by developing confusion matrices and using probabilistic metrics such as area under curve (AUC) score, F-1score, and Accuracy scores. Random forest outperformed the other models (AUC, F1, and Accuracy scores of 1.0). Probability threshold tuning was performed on the random forest model, and susceptibility maps with different thresholds were evaluated. An optimal threshold of 0.75 was used to balance false negatives and false positives in the predicted results, ensuring more reliable identification of hazard-prone slopes. The trained RF model revealed that the elevation, distance from streams, the NDVI, and precipitation were the top four factors influencing HWS failures in this study. The method allows for easy identification of vulnerable HWS across vast geographic areas. This method helps in effective fund utilization by doing targeted interventions and preventative maintenance efforts. Transportation agencies can implement this methodology on HWS at any location to strategize geotechnical asset management efforts.

A case study discovering lock-in effects of culinary culture and behaviours on cooking energy use in Chinese homes

Abstract China’s rapid urbanization and industrialization have expanded building floorspace and contributed to rising carbon emissions in the building sector. An often-overlooked aspect of residential energy use is cooking, which this study examines through two longitudinal household case studies, combined with a questionnaire survey of 202 households. Monitoring results show that cooking accounted for 23% and 48% of total household energy consumption in the two cases, confirming its significant contribution to the residential carbon footprint. To further investigate the observed highly linear growth in cooking energy use in both households, a questionnaire survey was further conducted, revealing a lock-in effect correlating cooking energy with family life cycle (FLC) stages and habitual cooking practices, rather than with family size per se. To quantify this relationship, this study proposes a novel indicator, Cooking Energy Use Intensity (CookEUI), defined as the average daily cooking energy consumption (kWh/day). CookEUI ranges from 4.13 to 5.10 kWh/day for elderly and middle-aged couples, increases to approximately 6–7 kWh/day for two-generation households, and reaches 8.13–12.86 kWh/day for three-generation households with dependent children. Survey responses further indicate that cooking energy is strongly constrained by culturally embedded culinary behaviours. Our findings suggest potential solutions to reduce cooking energy use and emissions – such as alternative cooking appliances, cleaner energy sources, and community dining options – while respecting entrenched culinary culture, providing valuable insights for sustainable residential cooking practice and supporting efforts to reduce household carbon emissions.

Field-based experimental investigation of energy and exergy performances of a novel solar thermal air collector

Characterization of the flavor profile and microbial-driven mechanism of characteristic flavor formation in Yuxi Taihe Douchi

Computational identification and mechanistic characterization of natural product binders targeting the PDE6D prenyl binding tunnel

Performance on the one-minute sit-to-stand test predicts long-term adverse outcomes in pulmonary hypertension

Deep neural network-based biostatistical analysis for disease marker screening

Spatial distribution and risk assessment of polychlorinated biphenyl compounds from open incineration of used medical disposable face masks

Purple LED light and crude glycerol synergistically enhance astaxanthin production in Aurantiochytrium limacinum

Satellite-based oil spill detection using an explainable ViR-SC hybrid deep learning ensemble for improved accuracy and transparency

Preliminary assessment of biodistribution and targeting of the fluorescent molecular probe Cy7-SYL3C in an EpCAM-positive colorectal cancer mouse model

Abstract Molecular imaging probes targeting the epithelial cell adhesion molecule (EpCAM) hold considerable promise in advancing colorectal cancer (CRC) research. Building on previous work, this study further evaluated the biodistribution of Cy7-SYL3C in healthy mice and its targeting efficacy in HT-29 colorectal cancer models, confirming its potential as a near-infrared fluorescent (NIRF) imaging probe. The fluorescent molecular probe Cy7-SYL3C was synthesized by conjugating the Cy7 fluorophore to the 5’ end of the SYL3C aptamer. Biodistribution studies were conducted in healthy mice following intravenous administration of the probe. For tumor targeting evaluation, a subcutaneous HT-29 human CRC xenograft model was established in nude mice. Tumor-bearing mice were allocated into two groups: an experimental group and a pre-blocking group. The pre-blocking group received an excess of unlabeled SYL3C aptamer prior to injection of Cy7-SYL3C. Small animal in vivo imaging technology (SAFI) was employed to monitor the biological distribution and tumor targeting ability of Cy7-SYL3C at different time points from 5 min to 48 h after injection. The expression of EpCAM in tumor tissues was analyzed by Western blot. The targeting ability of the probe was evaluated through immunofluorescence co-localization and pre-blocking protocols. Cy7-SYL3C is mainly metabolized and cleared by the liver and kidneys. Fluorescence signals can be detected at the tumor site only 5 min after injection. Quantitative analysis showed that the average fluorescence intensity (AFI) at the tumor site in the experimental group was 88.2% higher (6.4 × 10 7 photons/s/mm 2 ) compared to the pre-blocking group (3.4 × 10 7 photons/s/mm 2 ) over a 4-hour observation period. Furthermore, the experimental group displayed a moderate positive correlation (Pearson’s r  = 0.30 ± 0.02), in contrast to the negligible correlation observed in the pre-blocking group (Pearson’s r  = 0.05 ± 0.01). The tumor-to-muscle ratio exceeded 1.0 at six hours post-injection and peaked at 1.30 ± 0.04 photons/s/mm 2 , with target-specific signals maintained for up to eight hours. This indicates that as the probe gradually removed from normal tissues such as muscles, it achieved sustained and specific retention at the tumor site. Combined with the significant reduction in the signal caused by the pre-blocking strategy, these results consistently indicated that the accumulation of Cy7-SYL3C in the body exhibited EpCAM targeting specificity. This study was based on the EpCAM targeting strategy and further evaluated the in vivo performance of the near-infrared fluorescent probe Cy7-SYL3C. The detailed dynamic imaging results at multiple time points indicated that this probe had a clear metabolic pathway in healthy mice and demonstrated rapid, sustained and specific tumor targeting ability in the HT-29 colorectal cancer model. These characteristics collectively confirmed the clinical application potential of Cy7-SYL3C as a high-performance molecular imaging tool in colorectal cancer research.