Browse Articles

Hydrostatic sea-level rise inundation impacts on ahu and harbors of Rapa Nui (Easter Island)

Sea-level rise (SLR) threatens coastal communities, infrastructure, and cultural heritage worldwide, with small islands among the most vulnerable. Rapa Nui, renowned for its ahu and approximately 1,000 moai statues, faces dual risks: damage to irreplaceable cultural sites and disruption of modern harbors, both fundamental for maintaining cultural continuity and sustaining the island’s economy. This study evaluates the impacts of SLR on Rapa Nui’s coastal heritage and infrastructure using a probability-based hydrostatic inundation model. We establish present-day mean higher high water (MHHW) and the 1% annual exceedance probability (AEP) water level as reference baselines and simulate inundation extents at incremental SLR scenarios up to 3.9 m. Results indicate that four ahu are already impacted by present-day sea levels, and eight more are projected to be impacted by 2080. In the same timeframe, harbors are projected to be fully submerged under MHHW conditions. Comparisons between observed and modeled impacts further suggest that hydrostatic modeling inherently underestimates risk in Rapa Nui’s high-energy coastal environment. These results underscore the need for proactive adaptation strategies that integrate cultural heritage preservation with coastal resilience planning and improved hydrodynamic modeling to safeguard Rapa Nui’s identity, economy, and long-term sustainability.

Comparative clinical study of total thoracoscopic surgery and thoracoscopy-assisted small-incision surgery for multiple rib fractures

The explainability of radiomic-based machine learning models for brain glioma grading on amide proton transfer-weighted images

Association of statin use with pathological complete response in postmenopausal patients with hormone receptor–positive breast cancer

Abdominal obesity in India: sex stratified multilevel estimates across 707 districts from a nationally representative cross-sectional survey

Quercetin and nanoquercetin mitigate high fat diet–induced obesity via lipid modulation, genomic DNA integrity restoration, adipokine regulation, and hepato-pancreatic tissue preservation

Abstract Obesity is a global health challenge characterized by excessive fat accumulation and associated with life-threatening comorbidities such as type 2 diabetes, cardiovascular diseases, and certain cancers. Conventional treatments, including lifestyle modification and pharmacotherapy, often have limited long-term efficacy and potential side effects, highlighting the need for safer alternatives. Natural bioactive compounds, such as quercetin, a dietary flavonoid with antioxidant, anti-inflammatory, and metabolic regulatory properties, have emerged as promising anti-obesity agents. However, poor bioavailability limits its therapeutic application, prompting the development of nanoformulations. This study therefore estimated the anti-obesity potential of quercetin and nanoquercetin in a high-fat diet (HFD)-induced obesity model in male Wistar rats. Following acute toxicity testing, 36 rats were divided into six groups: non-obese control, obese HFD control, and non-obese or obese rats orally received quercetin or nanoquercetin at 10% of the safe dose daily for four weeks. Outcomes assessed included body weight, lipid profile, serum total protein, genomic DNA integrity, Adiponectin and Leptin gene expression, and histological changes in liver and pancreatic tissues. In non-obese rats, quercetin and nanoquercetin did not affect body weight and genomic DNA integrity but improved lipid profiles. Nanoquercetin additionally increased total protein levels. Both compounds upregulated Adiponectin expression in the liver, with nanoquercetin also enhancing pancreatic Adiponectin expression. Histology revealed preserved tissue architecture. In obese rats, administration of quercetin or nanoquercetin significantly reduced body weight, improved lipid and protein parameters, restored genomic DNA integrity, upregulated Adiponectin , downregulated Leptin , and markedly improved hepatic and pancreatic histological architecture. Nanoquercetin consistently produced more pronounced effects than quercetin.nIn. These findings demonstrate the therapeutic potential of quercetin, particularly its nanoform, as a multi-targeted anti-obesity agent. Its effects on metabolic regulation, genomic protection, and tissue preservation support further preclinical and clinical studies to explore its role as a safe and effective strategy for managing obesity.

Bacterially expressed recombinant TMOF induces mortality and gut microbial alterations in Aedes albopictus larvae

Assessment of environmental radioactivity in the City of Melilla

Detection and characterization of MPs in the human stool: an observational study in Bushehr, Iran

Optimizing photoperiod for growth and centellosides biosynthesis in Centella asiatica under vertical farming conditions

Abstract This study aimed to identify the optimal photoperiodic conditions for Centella asiatica , a medicinal plant known for its high-value bioactive compounds, including madecassoside and asiaticoside, in a vertical farming system. C. asiatica plants were cultivated for 4 weeks under varying photoperiods [20/4, 16/8, 12/12, and 8/16 h (light/dark)] with a light intensity of 200 ± 10 µmol·m − 2 ·s − 1 . The findings revealed that shoot fresh and dry weights, leaf length, leaf area, and leaf width were highest under 20/4, 16/8, and 12/12 h, while the number of runners peaked at 12/12 h. However, physiological disorders were observed under 20/4 and 16/8 h. Total phenol, total flavonoid, and antioxidant capacity increased linearly with photoperiod, reaching their maximum at 20/4 h and minimum at 8/16 h. Madecassoside and asiaticoside concentrations increased when the photoperiod exceeded 12/12 h, while madecassic acid and asiatic acid concentrations were higher under 8/16 h. Furthermore, light-use efficiency and energy-use efficiency, energy efficiency metrics, were highest at 12/12 h compared to other treatments. These findings indicate that the optimal photoperiod for maximizing biomass and secondary metabolite production in C. asiatica grown under 200 µmol·m − 2 ·s − 1 white light-emitting diodes in vertical farms is 12/12 h.

GC-MS profiling, biological activities and molecular docking of total sterol extracts from Pontederia crassipes (Lake Tana, Ethiopia)

Supervised machine learning intrusion detection review and multi-criteria evaluation

Exploring vision transformers for deep feature extraction and classification in video genre recognition for digital media

Abstract The generation of video content for television production using automation and artificial intelligence-based techniques is quite common these days. The use of computer vision techniques plays a significant role in the classification and analysis of large volumes of multimedia content. This study aims to develop an intelligent framework for TV genre classification using deep learning and advanced transformer-based models. Traditional machine learning depends on traditional features and lacks the ability to capture complex spatio-temporal and acoustic relationships in modern media. To address these limitations, the study explores state-of-the-art vision transformers for deeper analysis on two standard datasets in the relevant domain. Firstly, a static image dataset is analyzed using the Pyramid Vision Transformer (PvT), which effectively captures multi-scale spatial and contextual information across diverse TV scenes. Secondly, a multimodal audio–video dataset is used by applying the Multimodal Attention and Invariant Vision–Audio Representation Transformer (MAiVAR-T). The applied model captures temporal dependencies and integrates acoustic features, including mel-spectrogram, chroma, waveform, and energy patterns. Empirical analysis demonstrates that the proposed PvT and MAiVAR-T models achieve the highest accuracies of 97% and 98%, respectively, outperforming the baseline deep learning models. This study presents the role of multimodal transformers in improving automated genre classification in television and digital media production.

Multiple opsin expression in cubozoan ocelli indicates functional redundancy

Real-time decentralized model predictive control for cooperative multi-robot object transport: experimental validation

Abstract This paper presents an experimental validation of a decentralized Model Predictive Control (MPC) framework for cooperative object transportation utilizing a multi-robot system consisting of two mobile robots. Each robot is a differential-drive robot that independently solves local constrained optimization problems while ensuring global coordination through joint-space coupling. The formulation explicitly captures nonlinear kinematics, revolute-prismatic joint dynamics, inter-robot constraints, and dynamic obstacle avoidance within a real-time optimization setting. Adaptive weighting of cost terms is employed to balance trajectory tracking and formation objectives under varying task demands. The framework is deployed on a physical testbed integrating vision-based pose estimation, sensor fusion via a Kalman filter, and a ROS 2 control infrastructure. Experiments across point-to-point, curvilinear, and obstacle-rich scenarios show accurate trajectory tracking, strict constraint satisfaction, and robustness to environmental uncertainties. These results substantiate decentralized constrained MPC with adaptive weights as a practical and scalable solution for real-time multi-robot cooperative transport along arbitrary reference paths.

Assembling the self-depletion interaction puzzle

Why do hard spheres tend to stay together even though they experience strong repulsive forces upon contact? This intriguing phenomenon is widely recognized and has been precisely measured in the study of liquids. Here, we examine it within the framework of the integral equations theory of depletion forces, which offers a unique perspective. This approach provides new insights into the self-depletion effects among identical particles. We examine the phenomenon in monodisperse systems to clarify, from this viewpoint, why hard spheres tend to self-assemble into structured fluids. In binary mixtures, we examine how altering the concentration of one particle type affects the depletion interactions among these particles while also accounting for effects that go beyond the mediating influence of the other particle species. The main result of this contribution is that, by reformulating the theoretical framework around the notion of self-depletion, we develop an approach that represents any mixture of p components, at arbitrary concentrations, as an equivalent mixture of p + 1 components in which the additional component, composed of the particles under consideration, is present at a very low concentration. In this way, the general task of determining depletion potentials is reduced to the much simpler problem of computing the potential of mean force.

Microfluidic process-property correlations of dsRNA lipid nanoparticle formulations

Abstract The control of agricultural pest insects currently relies on broad-spectrum insecticides, which select for resistance in pest populations while also harming non-target species. In contrast, RNA interference (RNAi) has a species-dependent mode of action based on the delivery of double-stranded RNA (dsRNA) that precisely matches essential genes in pests, minimizing off-target effects. The successful application of RNAi requires the development of sprayable formulations that temporarily protect the dsRNA from environmental degradation (allowing uptake by pest insects) but also ensure the efficient release of the dsRNA within insect cells. Lipid nanoparticles (LNPs) based on pharmaceutical-grade lipids are currently too expensive for agricultural use, making the development of affordable and scalable dsRNA-LNP formulations essential for spray-induced gene silencing. Here we used technical-grade lipid components (available at the ton scale) and demonstrated the cost-effective production of structurally controlled dsRNA-LNP formulations by optimizing formulation recipes and scaling up the microfluidic mixing process. The dispersions contained spherical nanoparticles less than 100 nm in diameter, with a zeta potential exceeding + 20 mV, and an entropy-driven Gibbs free energy change for dsRNA-LNP decomplexation in the moderate range of approximately – 20 kJ/mol. The formulations protected dsRNA from RNase III degradation and hydrolysis at pH 4–11 for at least 24 h while allowing SDS-mediated dsRNA release. Our work provides insight into the structure–property correlations of inexpensive dsRNA-LNP formulations for sustainable RNAi-based pest management systems.

Quantifying the impact of the Tamm–Dancoff approximation on the computed spectra of transition-metal systems

The Tamm–Dancoff Approximation (TDA) offers a computationally efficient alternative to full linear-response Time-Dependent Density Functional Theory (TDDFT) for calculating electronic excited states, particularly in large molecular systems. By neglecting the coupling between excitation and de-excitation channels, TDA simplifies the TDDFT response equations into a Hermitian form. This not only reduces computational cost but also eliminates numerical instabilities that can arise in the full non-Hermitian formalism. While TDA has been widely explored for valence excitations, its reliability for transition metal complexes and core-level spectroscopies remains largely untested. In this work, we address this gap by systematically comparing TDA and full TDDFT results for a series of transition metal species, focusing on absorption spectra across the UV–Vis, metal K-edges, and L-edges. Our results show that, for core-level excitations, TDA yields excitation energies and oscillator strengths nearly indistinguishable from those obtained with full TDDFT. This agreement is attributed to the negligible contribution of de-excitation amplitudes at high excitation energies, indicating that the omitted coupling terms play an insignificant role in these spectral regimes.

AI-based detection of Certas Plus shunt valve settings in CT scans

Abstract Adjustable pressure cerebrospinal fluid (CSF) shunt valves are widely used in the treatment of hydrocephalus. CSF shunt dysfunctions can manifest with diverse symptoms, often requiring further diagnostic evaluation. Head computed tomography (CT) is frequently used as an initial diagnostic tool. Accurate identification of the current shunt valve setting is crucial for patient management; however, interpretation on CT is difficult due to three-dimensional imaging, metal artefacts, and limited spatial resolution. We therefore developed an artificial intelligence (AI)-based model to automatically assess shunt valve settings in CT scans. We collected 391 head CT scans from patients with CSF shunts featuring a Certas Plus valve. Shunt settings were extracted from medical records and verified on imaging. A 3D U-Net was trained to segment radiopaque valve components, from which the valve setting was inferred. The model successfully segmented valve components in 97.3% of test cases and correctly predicted the exact or an adjacent setting in 96% of cases. The segmentations enable clinicians to interpret and verify the prediction. Our study demonstrates the feasibility of AI-based detection of programmable shunt valve settings in CT scans. The proposed model reliably identifies Certas Plus valve settings and holds promise as a clinical support tool in shunt diagnostics.

Enhancing Gaussian process regression-accelerated QM/MM free energy simulations using atomic environment descriptors

Accurate free energy simulations based on combined quantum mechanical and molecular mechanical (QM/MM) potentials are essential for understanding reaction mechanisms in complex environments. Achieving ab initio QM/MM accuracy at the cost of more affordable semiempirical QM/MM methods, thereby enabling efficient sampling, remains a major challenge. To address this, we previously introduced a Δ-machine-learning approach employing Gaussian process regression (GPR) with QM-solute-based molecular descriptors. Here, we extend this approach by using atomic environment descriptors constructed from atom-centered symmetry functions, which incorporate MM-solvent contributions into the GPR input features. Molecular similarity is inferred through a system-specific sum kernel. We trained our models using both an energy-only GPR scheme and a GPR with derivative observation (GPRwDO) scheme that incorporates force information with heteroscedastic noise. On-the-fly model deployment in Chemistry at HARvard Macromolecular Mechanics (CHARMM)-based molecular dynamics simulations is enabled through a GPflow/pyCHARMM interface. We evaluated these approaches on the solution-phase SN2 Menshutkin reaction, using AM1/MM and B3LYP/MM as the base and target levels. The optimized models reduce AM1/MM potential energy errors from ∼13.1 to 1.4 (energy-only GPR) and 2.2 (GPRwDO) kcal/mol, with the corresponding force errors reduced from ∼14.6 to 4.4 and 2.1 (kcal/mol)/Å. The energy-only GPR model predicts a free energy barrier of 14.3 and a reaction free energy of −30.2 kcal/mol, whereas the GPRwDO model predicts 12.7 and −28.7 kcal/mol, both in excellent agreement with high-level benchmarks. Analyses of free energy paths, potentials of mean force, internal forces, and radial distribution functions reveal broad improvements in energetics, force description, and solvation structure. The AM1-GPR(wDO)/MM approaches reach target-level accuracy with an ∼100-fold acceleration.