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Effect of hydrogel rectal spacer on seminal vesicle inter-fraction motion during prostate stereotactic body radiotherapy

A non-genotoxic stem cell therapy boosts lymphopoiesis and averts age-related blood diseases in mice

Abstract Hematopoietic stem cell (HSC) transplantation offers a cure for a variety of blood disorders, predominantly affecting the elderly; however, its application, especially in this demographic, is limited by treatment toxicity. In response, we employ a murine transplantation model based on low-intensity conditioning protocols using antibody-mediated HSC depletion. While aging presents a significant barrier to effective HSC engraftment, optimizing HSC doses and non-genotoxic targeting methods greatly enhance the long-term multilineage activity of the transplanted cells. We demonstrate that young HSCs, once effectively engrafted in aged hosts, improve hematopoietic output and ameliorate age-compromised lymphopoiesis. This culminated in a strategy that robustly mitigates disease progression in a genetic model of myelodysplastic syndrome. These results suggest that non-genotoxic HSC transplantation could fundamentally change the clinical management of age-associated hematological disorders, offering a prophylactic tool to delay or even prevent their onset in elderly patients.

A hybrid PSO-FFNN approach for optimized seismic design and accurate structural response prediction in steel moment-resisting frames

The first steel is the most prevalent material used in building. Steel’s intrinsic hardness and durability make it appropriate for different uses, but its greater adaptability makes it ideal for seismic design. The brittle fracture occurred in welded moment connections of steel structures, which were originally thought to be ductile for resistance to earthquakes. The research aims to optimize structural parameters in steel structure seismic design. This paper presents an effective technique for the best seismic design of steel structures, which consists of two computational methodologies. First, particle swarm optimization (PSO) was presented to accurately define the structural characteristics in the seismic design of steel constructions, then a feed-forward neural network (FFNN) to determine unconventional seismic design methodologies for steel frameworks, precisely forecast the structural responses, and improve seismic resistance and dependability under dynamic conditions by using high-tech components and technological advancements. This study presents designing a realistic storey steel moment-resisting frame (MRF) structure and maximum weight under full seismic loading. The outcome demonstrates the reduction in generations that was accomplished during the optimization procedure. Although the PSO method in the paper converges in lower generations, the process indeed requires a significant amount of computing power. The FFNN approach involves the suggestion of a neural network model that works well to predict the necessary structural reactions during optimization. The proposed model considerably minimizes the total computation time. Study aims to improve the seismic analysis of steel using PSO along with forecasting structural responses using a network of feed-forward neural networks (FFNN) to enhance accuracy and reduce the computation time (2.4 min). The proposed FFNN model is more accurate than earlier methods, with the lowest MAPE values in S_IO (3.0661), S_LS (3.562), and S_CP (3.9252). Moreover, it reveals the highest predictive precision with the lowest RRMSE values of 0.0231 (S_IO), 0.0281 (S_LS), and 0.0314 (S_CP). Moreover, the FFNN model has a competitive run time of 2.4 minutes while possessing good goodness-of-fit, with 1.0096, 1.0995, and 0.9925 of R2 for S_IO, S_LS, and S_CP, respectively. As compared to WCFBP-RB, the proposed PSO+FFNN model has better prediction for S_IO, where the predicted value of 0.7879 is almost identical to the actual value of 0.8000. As compared to WCFBP-RB, the model predicts 1.4085 for S_LS, while the actual value is 1.4388. For S_CP, PSO+FFNN predicts 1.8621, which is more precise than WCFBP-RB and almost equals the actual figure of 1.9000.

MOCVD AlYN/GaN HEMTs with 66.5 mV/decade sub-threshold swing and 109 on/off ratio

We report the realization and operation of AlYN/GaN high-electron-mobility transistors (HEMTs). Metal-organic chemical vapor deposition is used to deposit AlYN/GaN semiconductor heterostructures on 100 mm SiC substrates. Polarization-induced 2D electron gas channels formed in the heterostructure exhibit room-temperature mobility >1300 cm2/V s, sheet density >1.4×1013/cm2, and sheet resistance <350 Ω/□ with good uniformity. Micron-long gate length HEMTs fabricated using regrown n+ GaN contacts demonstrate good DC performance with saturation drain current >0.4 A/mm, transconductance >0.3 S/mm, low threshold voltage VT=−2.0 V, and a high on/off ratio surpassing 109. The drain current shows a nearly negligible hysteresis with the sweep of the gate voltage. Notably, the devices exhibit a near-Boltzmann limit sub-threshold swing of 66.5 mV/dec. These observations highlight the promise of AlYN/GaN HEMTs for high-performance electronic applications.

Atom economic closed-loop recycling of thermoset polyurethane foams

KDTMD: Knowledge distillation for transportation mode detection based on KAN

With the progress in sensor technology and the spread of mobile devices, transportation mode detection (TMD) is gaining importance for health and urban traffic improvements. As mobile devices become more lightweight, they require more efficient, low-power models to handle limited resources effectively. Despite extensive research on TMD, challenges remain in capturing non-stationary temporal dynamics and nonlinear fitting capabilities. Additionally, many existing models exhibit high space complexity, making lightweight deployment on devices with limited computing and memory resources difficult. To address these issues, we propose a novel deep TMD model based on discrete wavelet transform (DWT) and knowledge distillation (KD), called KDTMD. This model consists of two main modules, i.e., DWT and KD. For the DWT module, since non-stationary time variations and event distribution shifts complicate sensor time series analysis, we use the DWT modules to disentangle the sensor time series into two parts: a low-frequency part that indicates the trend and a high-frequency part that captures events. The separated trend data is less influenced by event distribution shifts, effectively mitigating the impact of non-stationary time variations. For the KD module, it includes the teacher model and student model. Specifically, for teacher model, to address the nonlinearities and interpretability, we incorporate T-KAN, which is composed of multiple layers of linear KAN that employ learnable B-spline functions to achieve a richer feature representation with fewer parameters. For student model, we develop the S-CNN, which is trained efficiently by T-KAN through KD. The KDTMD model achieves 97.27% accuracy and 97.29% F1-Score on the SHL dataset, and 96.56% accuracy and 96.72% F1-Score on the HTC dataset. Additionally, the parameters of the KDTMD model are only about 10% of the smallest baseline.

Determining two-dimensional electron densities in AlGaN/GaN high electron mobility transistors using photoluminescence excitation

GaN/AlGaN high electron mobility transistors (HEMTs) are important devices due to their high-frequency and high-power applications. The two-dimensional (2D) electron density in AlGaN/GaN heterostructures is essential for the design of the state-of-the-art HEMTs. Here, the electro-absorption of GaN/AlGaN HEMTs was investigated using photoluminescence excitation (PLE), which is a sensitive and noncontact optical technique. The built-in electric fields of the GaN and AlGaN layers in HEMTs were derived from the Stark effect in PLE. According to the discontinuity in the displacement electric fields, the 2D electron density in the heterostructures can be estimated from PLE, which agrees with the values obtained from the Hall-effect measurements.

Vulnerability of power distribution networks to local temperature changes induced by global climate change

Abstract Global climate change (GCC) triggers a chain effect, converting temperature pattern changes into variations in blackout risk for power distribution grids (DGs). This occurs through GCC’s impacts on electricity supply, demand, and infrastructure, which shift the DG’s safe-operation boundary and power flow. This study presents a model integration framework to assess the associated blackout risk, showing that GCC raises blackout risks during peak hours by 4–6%, depending on Gross Domestic Product growth. Kirchhoff’s laws amplify these effects, creating nonlinear risk trajectories. Analysis of the chain effect suggests adaptation strategies, including reshaping grid topology and pairing temperature-sensitive users with robust buses. Index-based analysis reveals that over 20% of the U.S. requires at least a 10% DG capacity increase before 2050, with six states exceeding 20%. Europe faces a more moderate impact. These findings highlight the need for policymakers to prioritize peak-load management and address nonlinear risks across regions.

Complications of image-guided liver biopsies: Results of a nationwide database analysis

Background and aims Liver biopsy is the gold standard for evaluating liver diseases, the diagnosis of liver fibrosis or liver cirrhosis and malignancy. However, it is susceptible to complications, and safety data on liver biopsies remain scarce. The following study examined the complication rates following percutaneous liver biopsies. Methods We performed a study using data collected by the German interventional radiology society (DeGIR) from 2018 to 2021 of elective percutaneous liver biopsies. Clinical and hematological parameters, technical features and adverse events were retrospectively examined. Results From 2018 to 2021, a total of 12117 percutaneous liver biopsies were performed in 194 participating centers in Germany. Complications occurred in 235 biopsies (1.9%), of which 195 (1.6%) were major adverse events. Minor complications in the form of procedural hypotension and pain occurred in 7 and 33 cases (0.06% and 0.3%, respectively). Major complications such as bleeding, organ injury and pneumothorax were observed in 166, 3 and 26 cases (1.4%, 0.02% and 0.2%). Three subjects (0.02%) died as a result of massive intraperitoneal bleeding. Major and bleeding complications were significantly more frequently observed in patients with thrombocytopenia (p < 0.001) as well as in patients undergoing computed tomography (CT)-guided procedure compared to ultrasound-guided one (p < 0.001). Moreover, general and bleeding complication rates significantly differed by the liver segment biopsied (p < 0.001). In contrast, the type of needle size used (p = 0.323), internationalized ratio (INR) (p = 0.09), aPTT (p = 0.98), gender (p = 0.83), age (p = 0.08) and the number of biopsies (p = 0.91) performed did not impact the frequency of major adverse events. By multivariate logistic regression analysis, platelet count, the imaging modality used (CT vs. ultrasound-guided) and the liver segment biopsied were identified as independent risk factors of post-biopsy bleeding (p < 0.001 each). Conclusion Percutaneous liver biopsies are safe with rare procedural morbidity. Our data confirm previous data by showing that post-procedural bleeding was not associated with INR and aPTT in patients undergoing invasive procedures. However, measurement of platelet count is indicated to identify patients with increased procedural bleeding risk. Moreover, our findings suggest that patients with liver cirrhosis as well as patients with complex findings and difficult localizations could benefit from intensified monitoring post-procedural.

Semipolar (11 2¯ 2) AlN/AlGaN quasi-vertical Schottky barrier diodes grown on m-sapphire

In this work, we demonstrate the semipolar (11 2¯ 2) AlN/AlGaN Schottky barrier diodes (SBDs) on m-plane sapphire by metal organic chemical vapor deposition. Owing to the combination of high quality ultra-wide bandgap aluminum nitride (AlN) as the drift layer and AlGaN as the current spreading layer, the quasi-vertical (11 2¯ 2) AlN/AlGaN SBDs exhibit impressive characteristics with a low room temperature ideal factor of 2.05, a high barrier height of 1.89 eV, a rectification ratio of more than 108, the current density of larger than 1 A/cm2, the low specific on-resistance Ron,sp of about 3 Ω.cm2, and the low leakage current of about 1.22 × 10−5 mA/cm2 at 200 V reverse voltage. Especially, the performance of (11 2¯ 2) AlN/AlGaN quasi-vertical SBDs is substantially better than that of single-layer (11 2¯ 2) AlN or AlGaN lateral SBDs, and even comparable to the state-of-the-art polar c-plane AlN-based SBDs reported so far. This work marks a significant milestone in advancing semipolar AlN-based SBDs and shows a promising potential for yielding high-performance ultra-wide bandgap power devices.

Copper-catalyzed C(sp3)−H amination and etherification of unactivated hydrocarbons via photoelectrochemical pathway

Molecular insights into the role of Estrogen Receptor Beta in Ecdysterone Mediated Anabolic Activity

Ecdysterone, often dubbed a “natural steroid,” has garnered significant attention among athletes for its reputed growth-promoting and anabolic properties. Unlike synthetic anabolic steroids, which are classified as controlled substances, ecdysteroids remain largely unregulated in many countries and are widely marketed as dietary supplements. Notably, ecdysterone has been included in the World Anti-Doping Agency (WADA) monitoring program, highlighting its potential impact on athletic performance and raising questions about its regulation. Emerging evidence indicates that, unlike traditional anabolic steroids that act primarily via the Androgen Receptor (AR), ecdysterone’s anabolic effects may be mediated through Estrogen Receptors (ERs), particularly Estrogen Receptor beta (ERβ). Despite these insights, the precise molecular mechanisms underlying ecdysterone’s biological activity remain poorly characterized, particularly from an in-silico perspective. This paper aims to address these gaps by exploring ecdysterone’s mechanism of action through computational and molecular modeling approaches. This study employs an advanced computational framework to unravel the binding dynamics and interaction mechanisms of ecdysterone with Androgen Receptor (AR), Estrogen Receptor alpha (ERα), and Estrogen Receptor beta (ERβ). Using chemical descriptor analysis, inter-molecular interaction mapping, and all-atom molecular dynamics simulations spanning 250 ns for each system, the study reveals that ecdysterone preferentially binds to ERβ, forming stable and compact complexes characterized by minimal per-residue fluctuations as evident in the average RMSD, RMSF, and Rg values observed for ERβ - Ecdysterone as 1.98 ± 0.31 Å, 1.07 ± 0.52 Å, and 18.44 ± 0.08 Å respectively which are significantly comparable with the ERβ - native complex, while high hydrogen bond occupancy was also observed for ERβ - Ecdysterone complex. Although binding free energy calculations suggest stronger interactions with ERα, the associated high fluctuations diminish its binding efficacy. In contrast, interactions with ERβ remain consistent and robust. Machine learning-based principal component analysis highlights coordinated motion patterns, while free energy profiles demonstrate stable energy basins with minimal variation. These findings underscore the pivotal role of ERβ in mediating ecdysterone’s anabolic effects, distinguishing it from traditional androgenic steroids, and provide critical insights into its unique mechanism of action. This work lays the foundation for further exploration of ecdysterone as a potential anabolic agent.

Origin of magnetic switching cascades in nanostructured Co3Fe tetrapods

We present a comprehensive study of three-dimensional arrays of nanostructured Co3Fe tetrapods consisting of four pillars each with tetrahedral symmetry, prepared by focused electron beam-induced deposition and placed in two distinct orientations with respect to the direction of an external magnetic field. Using ultra-sensitive micro-Hall magnetometry, we obtain angular-dependent magnetic stray field hysteresis loops, which reveal a characteristic yet complex magnetization reversal behavior of the structures during a field sweep. We find that micromagnetic simulations significantly deviate from the experimental findings due to inherent limitations of the method. As an alternative approach, we derive a macrospin model that enables us to elucidate the observed hysteresis curves through the cascading switching dynamics of dipolar-coupled magnetic grains.

Author Correction: Multi-institutional atlas of brain metastases informs spatial modeling for precision imaging and personalized therapy

Effects of organic fertilizer replacing chemical fertilizer on organic carbon mineralization and active carbon fractions in yellow paddy soil of Guizhou Province

The aim was to decrease chemical fertilizer use and improve soil carbon sequestration. Replacing 50% chemical nitrogen fertilizer with organic fertilizer can inhibit the mineralization of organic carbon in yellow paddy soil by increasing the active organic carbon components. Four fertilization treatments (no fertilization, conventional fertilization, 50% organic fertilization and 50% chemical nitrogen fertilization, and organic fertilization instead of chemical nitrogen addition) were used to investigate the effects of using organic fertilizer instead of chemical fertilizer on soil organic carbon mineralization and active organic carbon components in paddy fields. The soil organic carbon, total nitrogen, available phosphorus, and available potassium contents were markedly higher for the organic fertilizer treatment than the no fertilization treatment. Compared with the application of chemical fertilizer alone, the substitution of chemical fertilizer with organic fertilizer significantly increased soil pH and significantly decreased the content of available potassium. The cumulative soil organic carbon mineralization rates for all treatments decreased during the incubation period. The ROC, dissolved organic carbon, and MBC contents were in 24.46%, 55.45%, and 17.60% higher, respectively, before and 19.34%, 74.98%, and 66.83%, respectively, after mineralization for 50% organic fertilization than no fertilization. Compared with the single application of chemical fertilizer, the ROC and DOC in the 1/2NPKM treatment increased significantly by 10.32% and 56.03% respectively after mineralization (p < 0.05), while the MBC in the M treatment decreased significantly by 12.05% before and 27.05% after mineralization (p < 0.05). The decrease in ROC was the most significant. Soil organic carbon mineralization was negatively correlated with SOC and active carbon fractions, and SOC was positively correlated with active carbon fractions. In summary, replacing 50% of chemical fertilizer with organic fertilizer inhibited soil organic carbon mineralization, which would improve carbon sequestration and fertilization. ROC and MBC were the main organic carbon sources mineralized.

Editorial for the Special Topic: Advances in quantum metrology

Cell-cell communication-mediated cell-type-specific parent-of-origin effects in mammals

Examination of the psychometric properties of Arabic version of the Body Vigilance Scale

Introduction The Body Vigilance Scale (BVS) was designed and validated as a short and concise measure to assess attentional focus on bodily sensations and related processes. The BVS is available in the English language, but no Arabic version have been developed, and no validation of the scale exists in Lebanon. The current study aimed to determine the reliability, validity and factor structure of the Arabic version of the Body Vigilance Scale. Methods This study has a cross-sectional design. It was conducted from October 2 to November 20, 2024, enrolling Lebanese adults. The study was carried out in the Arabic language and included the BVS, the Patient Health Questionnaire, the Insomnia Severity Index and the Freiburg Mindfulness Inventory. Results In total, 641 participants participated in this study, with a mean age of 35.11 ± 12.67 years and 70.5% females. Internal reliability of BVS was adequate (ω = .87/ α = .86). Invariance was shown at the metric and scalar levels in terms of genders. A significantly higher mean BVS score was found in females compared to males. Higher depression (r = 0.26; p < 0.001), anxiety (r = 0.29; p < 0.001), insomnia (r = 0.29; p < 0.001) and mindfulness (r = 0.27; p < 0.001) correlated significantly with higher body vigilance scores. Conclusion The Arabic version of the BVS is a reliable and valid tool for assessing somatic attention in Arabic-speaking populations. Its psychometric robustness, demonstrated measurement invariance across genders, and associations with psychological distress measures underscore its utility in both clinical and research settings.

Polarization-resolved terahertz metasurface sensor for concentration sensing and isomer recognition of chiral molecules

The paper presents a terahertz polarization sensing method based on a metasurface for concentration and isomer recognition of chiral molecules with high sensitivity. By designing a metasurface sensor with a spiral square ring structure and combining it with a polyvinyl alcohol-proline thin film coating, the circular dichroism and optical rotation signals of the sample are extracted using a terahertz polarization-resolved time-domain spectroscopy system. The experimental results show that the metasurface significantly improves the chiral signal detection sensitivity (4.57 GHz/(μg/cm2)), and its figure of merit (0.0354 cm2/μg) is superior to previous ring structure sensors. Further analysis shows the polarization ellipse angle and polarization rotation angle of different chiral molecules (D/L-proline) exhibit differentiated characteristics. This method provides a strategy for the precise detection of ultra-weak chiral signals in the terahertz radiation.

Concept transfer of synaptic diversity from biological to artificial neural networks

Abstract Recent developments in artificial neural networks have drawn inspiration from biological neural networks, leveraging the concept of the artificial neuron to model the learning abilities of biological nerve cells. However, while neuroscience has provided new insights into the mechanisms of biological neural networks, only a limited number of these concepts have been directly applied to artificial neural networks, with no guarantee of improved performance. Here, we address the discrepancy between the inhomogeneous and dynamic structures of biological neural networks and the largely homogeneous and fixed topologies of artificial neural networks. Specifically, we demonstrate successful integration of concepts of synaptic diversity, including spontaneous spine remodeling, synaptic plasticity diversity, and multi-synaptic connectivity, into artificial neural networks. Our findings reveal increased learning speed, prediction accuracy, and resilience to gradient inversion attacks. Our publicly available drop-in replacement code enables easy incorporation of these proposed concepts into existing networks.