Search Results (44)

Proton therapy enables precise dose delivery to tumors while sparing healthy tissues, offering significant advantages over conventional radiotherapy. Accurate prediction of biological doses requires detailed knowledge of radiation interactions with biological targets, especially DNA, a key site of radiation-induced damage. While most biophysical models (LEM, mMKM, NanOx) rely on water as a surrogate, this simplification neglects the complexity of real biomolecules. In this work, we calculate the stopping power and range of protons in liquid water, dry DNA, and hydrated DNA using semi-empirical cross sections for ionization, electronic excitation, electron capture, and electron loss by protons and neutral hydrogen in the 10 keV–100 MeV energy range. Additionally, ionization cross sections for uracil are computed to explore potential differences between DNA and RNA damage. Our results show excellent agreement with experimental and ab initio data, highlighting significant deviations in stopping power and range between water and DNA. Notably, the stopping power of DNA exceeds that of water at most energies, reducing proton ranges in dry and hydrated DNA by up to 20% and 26%, respectively. These findings provide improved input for Monte Carlo simulations and biophysical models, enhancing RBE predictions and dose accuracy in hadrontherapy. Full article

This study investigates the dynamic response mechanisms of discharge capacity in the Han River Estuary to hydrological process changes at the Yangtze–Han River confluence. By constructing a one-dimensional hydrodynamic model for the 265 km Xinglong–Hankou reach, we quantitatively decouple the synergistic effects of riverbed scouring (mean annual incision rate: 0.12 m) and Three Gorges Dam (TGD) operation through four orthogonal scenarios. Key findings reveal: (1) Riverbed incision dominates discharge variation (annual mean contribution >84%), enhancing flood conveyance efficiency with a peak flow increase of 21.3 m³/s during July–September; (2) TGD regulation exhibits spatiotemporal intermittency, contributing 25–36% during impoundment periods (September–October) by reducing Yangtze backwater effects; (3) Nonlinear interactions between drivers reconfigure flow paths—antagonism occurs at low confluence ratios (R < 0.15, e.g., Cd increases to 45 under TGD but decreases to 8 under incision), while synergy at high ratios (R > 0.25) reduces Hanchuan Station flow by 13.84 m³/s; (4) The 180–265 km confluence-proximal zone is identified as a sensitive area, where coupled drivers amplify water surface gradients to −1.41 × 10⁻³ m/km (2.3× upstream) and velocity increments to 0.0027 m/s. The proposed “Natural/Anthropogenic Dual-Stressor Framework” elucidates estuary discharge mechanisms under intensive human interference, providing critical insights for flood control and trans-basin water resource management in tide-free estuaries globally. Full article

Pavement performance models are considered a key element in pavement management systems since they can predict the future condition of pavements using historical data. Several indicators are used to evaluate the condition of pavements (such as the pavement condition index, rutting depth, and cracking severity), and the international roughness index (IRI), which is the most widely employed worldwide. This study aimed to develop an accurate IRI prediction model. Ten prediction methods were trained on a dataset of 35 independent variables. The performance of the methods was compared, and the light gradient boosting machine was identified as the best-performing method for IRI prediction. Then, the SHAP was synchronized with the best-performing method to prioritize variables based on their relative influence on IRI. The results suggested that initial IRI, mean annual temperature, and the duration between data collections had the strongest relative influence on IRI prediction. Another objective of this study was to determine the optimal uncertainty model for IRI prediction. In this regard, 12 uncertainty models were developed based on different conformal prediction methods. Gray relational analysis was performed to identify the optimal uncertainty model. The results showed that Minmax/80 was the optimal uncertainty model for IRI prediction, with an effective coverage of 93.4% and an average interval width of 0.256 m/km. Finally, a further analysis was performed on the outcomes of the optimal uncertainty model, and initial IRI, duration, annual precipitation, and a few distress parameters were identified as uncertain. The results of the framework indicate in which situations the predicted IRI may be unreliable. Full article

The rapid growth in scientific knowledge has created a critical need for advanced systems capable of managing mathematical knowledge at scale. This study presents a novel approach that integrates ontology-based knowledge representation with large language models (LLMs) to automate the extraction, organization, and reasoning of mathematical knowledge from LaTeX documents. The proposed system enhances Mathematical Knowledge Management (MKM) by enabling structured storage, semantic querying, and logical validation of mathematical statements. The key innovations include a lightweight ontology for modeling hypotheses, conclusions, and proofs, and algorithms for optimizing assumptions and generating pseudo-demonstrations. A user-friendly web interface supports visualization and interaction with the knowledge graph, facilitating tasks such as curriculum validation and intelligent tutoring. The results demonstrate high accuracy in mathematical statement extraction and ontology population, with potential scalability for handling large datasets. This work bridges the gap between symbolic knowledge and data-driven reasoning, offering a robust solution for scalable, interpretable, and precise MKM. Full article

This study examined genetic diversity in the endangered medicinal plant Celastrus paniculatus using 62 individual samples from seven natural populations in northern and northeastern Thailand to inform conservation strategies. The analysis of the nuclear internal transcribed spacer (ITS) and ribulose-1,5-bisphosphate carboxylase/oxygenase large subunit (rbcL) markers revealed 17 haplotypes (CpI1–CpI17) across these populations, with 15 being population-specific. The genetic diversity varied significantly among populations: CMI showed the highest diversity (Hd = 0.944 ± 0.070), while LEI and LPN displayed complete homogeneity. The haplotype network identified a central shared haplotype (CpI4), suggesting a common ancestry, with the PLK population showing a distinct genetic divergence through unique haplotypes separated by multiple mutation steps. Genetic distance calculations revealed close relationships between LEI and NPM populations (distance = 0.0004), with greater differentiation between PLK and other populations (distances > 0.005). Phylogenetic analyses confirmed the species integrity while highlighting population clusters, especially PLK in ITS analyses and LPN in rbcL analyses. This genetic structure information provides a foundation for targeted conservation planning. Results suggest that conservation efforts should prioritize both genetically diverse populations (like CMI and MKM) and genetically distinct ones (like PLK) to preserve the maximum evolutionary potential. This study delivers crucial molecular data for developing evidence-based conservation strategies to protect this valuable medicinal species from further decline. Full article

Microdosimetry plays a critical role in particle therapy by quantifying energy deposition within microscopic domains to assess biological effects. This study evaluates the influence of different microdosimetric functions (MFs) and domain geometries (DGs) on relative biological effectiveness (RBE) predictions in carbon ion radiotherapy. Specifically, we compare the analytical microdosimetric function (AMF), calculated for spherical domains and implemented in PHITS, with the Kiefer–Chatterjee (KC) track structure model, which is conventionally applied to cylindrical geometries. To enable a direct comparison, we also introduce a novel implementation of the KC model for spherical domains. Using both models, specific energy distributions were calculated across a range of domain sizes and geometries. These distributions were input into the modified microdosimetric kinetic model (mMKM) to calculate RBE for the HSG cell line and compared against published in vitro data. The results show that both microdosimetric function and domain geometry significantly affect microdosimetric spectra and the resulting RBE, with deviations exceeding 10% when fixed mMKM parameters are used. Parameter optimization within the mMKM enables alignment across models. Our findings emphasize that microdosimetric function and domain geometry selection must be explicitly accounted for in microdosimetry-based RBE modeling, and that model parameters must be tuned accordingly to ensure consistent and biologically accurate predictions. Full article

Following unfavorable environmental cues, cells reprogram pathways that govern transcription, translation, and protein degradation systems. This reprogramming is essential to restore homeostasis or commit to cell death. This review focuses on the secondary roles of two nuclear transcriptional regulators, cyclin C and Med13, which play key roles in this decision process. Both proteins are members of the Mediator kinase module (MKM) of the Mediator complex, which, under normal physiological conditions, positively and negatively regulates a subset of stress response genes. However, cyclin C and Med13 translocate to the cytoplasm following cell death or cell survival cues, interacting with a host of cell death and cell survival proteins, respectively. In the cytoplasm, cyclin C is required for stress-induced mitochondrial hyperfission and promotes regulated cell death pathways. Cytoplasmic Med13 stimulates the stress-induced assembly of processing bodies (P-bodies) and is required for the autophagic degradation of a subset of P-body assembly factors by cargo hitchhiking autophagy. This review focuses on these secondary, a.k.a. “night jobs” of cyclin C and Med13, outlining the importance of these secondary functions in maintaining cellular homeostasis following stress. Full article

The increasing demand for sustainable road infrastructure necessitates alternative materials that enhance soil stabilization while reducing environmental impact. This study investigated the application of organosilane-based nanotechnology to improve the structural performance and durability of road corridors in Peru, offering a viable alternative to conventional stabilization methods. A comparative experimental approach was employed, where modified soil and asphalt mixtures were evaluated against control samples without nanotechnology. Laboratory tests showed that organosilane-treated soil achieved up to a 100% increase in the California Bearing Ratio (CBR), while maintaining expansion below 0.5%, significantly reducing moisture susceptibility compared to untreated soil. Asphalt mixtures incorporating nanotechnology-based adhesion enhancers exhibited a Tensile Strength Ratio (TSR) exceeding 80%, ensuring a superior resistance to moisture-induced damage relative to conventional mixtures. Non-destructive evaluations, including Dynamic Cone Penetrometer (DCP) and Pavement Condition Index (PCI) tests, confirmed the improved long-term durability and load-bearing capacity. Furthermore, statistical analysis of the International Roughness Index (IRI) revealed a mean value of 2.449 m/km, which is well below the Peruvian regulatory threshold of 3.5 m/km, demonstrating a significant improvement over untreated pavements. Furthermore, a comparative reference to IRI standards from other countries contextualized these results. This research underscores the potential of nanotechnology to enhance pavement resilience, optimize resource utilization, and advance sustainable construction practices. Full article

McKibben’s muscle (MKM) is the most adopted among the different types of pneumatic artificial muscles (PAMs) due to its mechanical performance and versatility. Several geometric parameters, including the diameter, thickness, and length of the inner elastic element, as well as functional conditions, such as shortening ratio and feeding pressure, influence the behaviour of this actuator. Over the years, analytical and numerical models have been defined to predict its deformation and developed forces. However, these models are often identified under simplifications and have limitations when integrating new parameters that were not initially considered. This work proposes a hybrid approach between finite element analyses (FEAs) and machine learning (ML) algorithms to overcome these issues. An MKM was numerically simulated as the chosen parameters changed, realizing the MKM dataset. The latter was used to train 27 artificial neural networks (ANNs) to identify the best algorithm for predicting the developed forces. The best ANN was tested on three numerical models and a prototype with a combination of parameters not included in the dataset, comparing predicted and numerical responses. The results demonstrate the effectiveness of ML techniques in predicting the behavior of MKMs while offering flexibility for integrating additional parameters. Therefore, this paper highlights the potential of ML approaches in the mechanical design of MKM according to the field of use and application. Full article

Ion radiotherapy requires accurate relative biological effectiveness (RBE) calculations to account for the markedly different biological effects of ions compared to photons. Microdosimetric RBE models rely on descriptions of the energy deposition at the microscopic scale, either through radial dose distributions (RDDs) or microdosimetric probability density distributions. While RDD approaches focus on the theoretical description of the energy deposition around the ion track, microdosimetric distributions offer the advantage of being experimentally measurable, which is crucial for quality assurance programs. As the results of microdosimetric RBE models depend on whether RDD or microdosimetric distributions are used, the model parameters are not interchangeable between these approaches. This study presents and validates a method to reproduce the published reference biological and clinical dose calculations at NIRS-QST for only carbon ion radiotherapy by using the modified microdosimetric kinetic model (MKM) alongside microdosimetric distributions instead of the reference RDD approach. To achieve this, Monte Carlo simulations were performed to estimate the variation of the radiation quality within and outside the field of pristine and spread-out Bragg peaks. By appropriately optimizing the modified MKM parameters for microdosimetric distributions assessed within water spheres, we successfully reproduced the results of calculations using the reference NIRS-QST RDD, generally within 2%. Full article

A constant proton relative biological effectiveness (RBE) of 1.1 for tumor control is currently used in proton therapy treatment planning. However, in vitro, in vivo and clinical experiences indicate that proton RBE varies with kinetic energy and, therefore, tissue depth within proton Bragg peaks. A number of published RBE models capture variations in proton RBE with depth. The published models can be sub-divided into empirical (or phenomenological) and biophysical (or mechanistic-inspired) RBE models. Empirical RBE models usually characterize the beam quality through the dose-averaged linear energy transfer (${\mathrm{LET}}_{\mathrm{D}}$), while most biophysical RBE models relate RBE to the dose-averaged lineal energy ($y_{\mathrm{D}}$). In this work, an analytic microdosimetry model and the Monte Carlo damage simulation code (MCDS) were utilized for the evaluation of the ${\mathrm{LET}}_{\mathrm{D}}$ and $y_{\mathrm{D}}$ of monoenergetic proton beams in the clinically relevant energy range of 1–250 MeV. The calculated ${\mathrm{LET}}_{\mathrm{D}}$ and $y_{\mathrm{D}}$ values were then used for the estimation of the RBE for five different cell types at three dose levels (2 Gy, 5 Gy and 7 Gy). Comparisons are made between nine empirical RBE models and two biophysical models, namely, the theory of dual radiation action (TDRA) and the microdosimetric kinetic model (MKM). The results show that, at conventional dose fractions (~2 Gy) and for proton energies which correspond to the proximal and central regions of the spread-out Bragg peak (SOBP), RBE varies from 1.0 to 1.2. At lower proton energies related to the distal SOBP, we find significant deviations from a constant $\mathrm{RBE}$ of 1.1, especially for late-responding tissues (low ${(\alpha /\beta )}_R$ of ~1.5–3.5 Gy) where proton RBE may reach 1.3 to 1.5. For hypofractionated dose fractions (5–7 Gy), deviations from a constant $\mathrm{RBE}$ of 1.1 are smaller, but may still be sizeable, yielding RBE values between 1.15 and 1.3. However, large discrepancies among the different models were observed that make the selection of a variable RBE across the SOBP uncertain. Full article

Background: Pancreatic cancer is one of the most aggressive and lethal cancers. New treatment strategies are highly warranted. Particle radiotherapy could offer a way to overcome the radioresistant nature of pancreatic cancer because of its biological and physical characteristics. Within particles, helium ions represent an attractive therapy option to achieve the highest possible conformity while at the same time protecting the surrounding normal tissue. The aim of this study was to evaluate the cytotoxic efficacy of helium ion irradiation in pancreatic cancer in vitro. Methods: Human pancreatic cancer cell lines AsPC-1, BxPC-3 and Panc-1 were irradiated with photons and helium ions at various doses and treated with gemcitabine. Photon irradiation was performed with a biological cabin X-ray irradiator, and helium ion irradiation was performed with a spread-out Bragg peak using the raster scanning technique at the Heidelberg Ion Beam Therapy Center (HIT). The cytotoxic effect on pancreatic cancer cells was measured with clonogenic survival. The survival curves were compared to the predicted curves that were calculated via the modified microdosimetric kinetic model (mMKM). Results: The experimental relative biological effectiveness (RBE) of helium ion irradiation ranged from 1.0 to 1.7. The predicted survival curves obtained via mMKM calculations matched the experimental survival curves. Mainly additive cytotoxic effects were observed for the cell lines AsPC-1, BxPC-3 and Panc-1. Conclusion: Our results demonstrate the cytotoxic efficacy of helium ion radiotherapy in pancreatic cancer in vitro as well as the capability of mMKM calculation and its value for biological plan optimization in helium ion therapy for pancreatic cancer. A combined treatment of helium irradiation and chemotherapy with gemcitabine leads to mainly additive cytotoxic effects in pancreatic cancer cell lines. The data generated in this study may serve as the radiobiological basis for future experimental and clinical works using helium ion radiotherapy in pancreatic cancer treatment. Full article

The as-built roughness, or smoothness obtained during pavement construction, plays an important role in road engineering since it serves as an indicator for both the level of service provided to users and the overall standard of construction quality. Being able to predict as-built roughness is therefore important for supporting pavement design and management decision making. An as-built IRI prediction model for asphalt overlays based on profile transformation was proposed in a previous study. The model, used as basis for this work, was developed for the case of wheeled pavers without automatic screed levelling. This study presents further development of the base prediction model, including the use of an automatic screed control system through a long-distance averaging mobile reference. Formulation of linear systems that constitute the model are presented for the case of a wheeled paver using contactless acoustic sensors set-up over a floating levelling beam attached to the paver. To calibrate the model, longitudinal profile data from the Long-Term Pavement Performance SPS-5 experiment was used, obtaining a mean error of 0.17 m/km for the predicted IRI. The results obtained demonstrate the potential of the proposed approach as a modelling alternative. Full article

To minimize radiation-induced lumbosacral neuropathy (RILSN), we employed sacral-nerve-sparing optimized carbon-ion therapy strategy (SNSo-CIRT) in treating 35 patients with pelvic sarcomas/chordomas. Plans were optimized using Local Effect Model-I (LEM-I), prescribed D_{RBE|LEM-I|D50%} (median dose to HD-PTV) = 73.6 (70.4–76.8) Gy (RBE)/16 fractions. Sacral nerves were contoured between L5-S3 levels. D_RBE|LEM-I to 5% of sacral nerves-to-spare (outside HD-CTV) (D_{RBE|LEM-I|D5%}) were restricted to <69 Gy (RBE). The median follow-up was 25 months (range of 2–53). Three patients (9%) developed late RILSN (≥G3) after an average period of 8 months post-CIRT. The RILSN-free survival at 2 years was 91% (CI, 81–100). With SNSo-CIRT, D_{RBE|LEM-I|D5%} for sacral nerves-to-spare = 66.9 ± 1.9 Gy (RBE), maintaining D_RBE|LEM-I to 98% of HD-CTV (D_{RBE|LEM-I|D98%}) = 70 ± 3.6 Gy (RBE). Two-year OS and LC were 100% and 93% (CI, 84–100), respectively. LETd and D_RBE with modified-microdosimetric kinetic model (mMKM) were recomputed retrospectively. D_RBE|LEM-I and D_RBE|mMKM were similar, but D_RBE-filtered-LETd was higher in sacral nerves-to-spare in patients with RILSN than those without. At D_RBE|LEM-I cutoff = 64 Gy (RBE), 2-year RILSN-free survival was 100% in patients with <12% of sacral nerves-to-spare voxels receiving LETd > 55 keV/µm than 75% (CI, 54–100) in those with ≥12% of voxels (p < 0.05). D_RBE-filtered-LETd holds promise for the SNSo-CIRT strategy but requires longer follow-up for validation. Full article

Sediment precipitation in riverbeds influences the effectiveness of structural and non-structural measures for flash flood mitigation and increases the potential for flooding. This study aimed to disclose the effectiveness of the implemented measures for flood risk mitigation in Kassala town, eastern Sudan. We employed remote sensing (RS) and GIS techniques to determine the change in the Gash River riverbed, the morphology, and the leveling of both the eastern and western sides of the river. Flood model simulation and a 3D path profile were generated using the digital elevation model (DEM) with a data resolution of 12.5 m from the ALOS BILSAR satellite. The main purpose of this study is to extract the layer of elevation of the riverbed on both the western and eastern banks and to determine the variations and their relationship to flood occurrence and mitigation. The construction of dikes and spurs near Kassala town has led to sediment precipitation, causing the riverbed to rise. The results show that it is now 1.5 m above the eastern Kassala town level, with a steep slope of 2 m/km, and the cross-section area at Kassala bridge has shrunk, which indicates that the bridge body will partially impede the river’s high discharge and increase the potential for flood risk in the study area. The eastern part of Kassala town has a higher likelihood of flooding than the western side. This study suggests redesigning structural measures like widening the Gash River, extending Kassala bridge for normal water flow, strengthening early warning systems, and implementing soil conservation activities for normal water flow. Full article