67639 research outputs found
Sort by
VarLand: A pipeline to map the structural landscape of missense variants at the proteome scale
No full textMissense variant pathogenicity often arises from disruptions to protein structural features. The integration of large-scale genetic sequencing into clinical workflows, and the availability of accurate AI-based protein structure predictions present an opportunity to assess the structure–function relationship of missense variants at a population scale. To harness this potential, we developed VarLand, a computational pipeline that extracts 29 structural and biophysical features from AlphaFold-predicted protein models and nine complementary annotation tools. We applied VarLand to pathogenic missense variants from ClinVar and a population-specific dataset of rare Middle Eastern variants, comparing their feature profiles to high-frequency benign variants from the Genome Aggregation Database (gnomAD). Our analysis confirms that pathogenic variants are significantly enriched in ordered regions, buried residues, and sites with high intramolecular contact density, whereas benign variants preferentially occur in disordered, solvent-exposed regions. However, VarLand also uncovered feature landscape variations across protein functional classes and disease categories, suggesting differences in underlying disease mechanisms. Furthermore, variants from the AI-based AlphaMissense database showed a stronger association between structural order and pathogenicity than clinical datasets, indicating residual bias from structure-centric training. These findings demonstrate the effectiveness of multidimensional structural profiling by VarLand to uncover not only broad structure–pathogenicity relationships but also dataset-specific and class-specific deviations, offering deeper insight into disease mechanisms.The research reported in this publication was supported by funding from King Abdullah University of Science and Technology (KAUST) through the baseline fund to STA, and the KAUST Center of Excellence for Smart Health (KCSH), under award number 5932. For computer time, this research used the resources of the KAUST Supercomputing Laboratory
Mapping Bubble Hydrodynamics in a Pseudo-2D Fluidized Bed Reactor with Multiple Injections and Diameters
No full textFluidized bed reactors are widely used in various industrial processes. An accurate description of hydrodynamic behaviors is essential for improving performance, sustainability, and profitability as the gas-solid contact pattern significantly influences heat and mass transfer efficiency. However, conventional hydrodynamic characterizations by high-speed cameras or probes are expensive. This research presents a cost-effective methodology for analyzing flow dynamics in a pseudo-2D fluidized bed reactor. By utilizing phosphorescent particles alongside advanced image processing techniques, this study enables the detailed visualization of gas-solid interactions. The good agreements between experimental results with empirical equations validate the proposed workflow. Based on this, we map the bubble trajectories and identify stagnant regions within the reactor under multiple feeding injectors. These findings contribute to a better understanding of the complex dynamic fluidization behavior, supporting improved reactor designs and operational efficiency in petrochemical applications
Platforms for Monitoring and Remediation of Crops for Precision Farming
No full textPrecision farming (PF) has emerged as a transformative approach to optimizing agricultural practices by integrating real-time monitoring and targeted interventions. This thesis presents the development of plant-integrated sensing and delivery platforms that seek to enhance the efficiency, sustainability, and adaptability of modern agriculture. The work primarily focuses on the design and application of minimally invasive microneedle (MN)-based sensors for real-time plant health assessment, as well as phyto-injector systems for targeted agrochemical and genetic cargo delivery. These technologies offer novel solutions to longstanding challenges in crop monitoring and disease management. The MN-based sensors developed in this thesis provide a direct interface with plant tissues, enabling the continuous detection of key phytohormones and stress biomarkers such as salicylic acid (SA) and indole-3-acetic acid (IAA). By employing electrochemical and impedimetric sensing techniques, this platform achieves high sensitivity and specificity in monitoring physiological changes, allowing for early disease detection and precision treatment strategies. The incorporation of molecularly imprinted polymers (MIPs) further enhances the selectivity of these sensors, paving the way for broader applications in plant metabolomics. Additionally, this thesis introduces a phyto-injector system designed to facilitate the localized and controlled release of bioactive compounds. This platform provides a scalable and tunable method for agrochemical application and genetic modification, reducing environmental contamination and improving treatment efficacy. The phyto-injector’s validation using Agrobacterium-mediated transient gene expression demonstrates its potential for applications in plant biotechnology and stress adaptation research. The results of this work highlight the potential of integrating advanced sensing and delivery systems into PF frameworks, potentially improving resource efficiency and agricultural resilience. These advancements offer scalable and field-deployable solutions that can enhance yield predictions, reduce agrochemical dependency, and support sustainable farming practices. Future advancements in wireless connectivity and data integration will further extend the impact of these platforms to realize the potential of PF. This thesis, therefore, contributes a step toward the widespread adoption of precision-based plant monitoring and remediation technologies
Revealing the mechanisms of ammonia dual-fuel combustion for decarbonization in marine transportation
No full textAmmonia dual-fuel combustion offers a promising pathway for decarbonization in marine transportation, but nitrogen oxide (NOx) emissions present a significant challenge. This study investigates ammonia dual-fuel combustion under engine and constant volume chamber conditions using high-fidelity large eddy simulations (LES). The implemented combustion model has been extensively validated under both fundamental and engine combustion conditions. A novel three-dimensional species budget analysis tool, developed and implemented for the first time, quantifies the roles of convection, diffusion, and chemical sources in species transport. Unlike previous studies relying on zero-dimensional simulations and chemical pathway analysis, this approach captures the coupled effects of turbulent mixing, and local transient flows, offering a more physically representative understanding. A multidimensional reaction pathway analysis reveals the influence of ammonia fraction on heat release and NOx formation. Distinct flame structures were observed: the constant volume chamber shows a standard jet flame, while the engine presents a distributed flame due to complex piston-flow interactions. Nitric oxide (NO) forms mainly in high-temperature regions (T > 2000 K), nitrous oxide (N2O) accumulates in low-temperature zones (T < 1200 K). Chemical reactions are found to dominate NO and N2O formation, though convection contributes over 30 % to N2O, with diffusion remaining minimal. Furthermore, the formation mechanisms of key combustion species were mapped using probability density functions, elucidating the role of thermal mixing during combustion and providing actionable insights for optimizing ammonia dual-fuel combustion strategies. These findings contribute to overcoming the NOx challenge and advancing sustainable, low-carbon technologies for marine transportation.The authors thank the support from King Abdullah University of Science and Technology. Dr. Qinglong Tang is funded by the National Natural Science Foundation of China through its project 52206166. Dr. L Xu is funded by the National Natural Science Foundation of China through its project 52406150. The computational simulations utilized the clusters of the KAUST Supercomputing Laboratory. The authors thank Convergent Science Inc. for providing the CONVERGE license
Redox-Responsive PEO-<i>b</i>-PCL-Based Block Copolymers for Synergistic Drug Delivery and Bioimaging in Cancer Cells.
No full textStimuli-responsive polymer-based nanocarriers enhance the drug delivery efficiency by enabling targeted release at tumor sites. However, integrating therapeutic and diagnostic functions into a single nanoplatform while maintaining control over both remains a significant challenge. This study presents a stimuli-responsive, multifunctional poly(ethylene oxide)-b-poly(ε-caprolactone) (PEO-b-PCL) nanocarrier for combination cancer therapy and bioimaging. The system codelivers chlorambucil (CHL) and methotrexate (MTX) to enhance therapeutic efficacy and overcome multidrug resistance. A redox-responsive disulfide linker enables CHL release in the tumor's glutathione-rich environment, ensuring selective drug activation. Additionally, an aggregation-induced emission (AIE) fluorophore, tetraphenylethylene (TPE), facilitates the monitoring of cellular uptake and drug release. The resulting TPE-(PEO-b-PCL)-S-S-CHL (P3) micelles encapsulated with MTX (P3-MTX) exhibited favorable size, morphology, and enhanced cytotoxicity, demonstrating a synergistic effect in combination therapy. Confocal laser scanning microscopy (CLSM) confirmed intracellular uptake by using TPE-based fluorescence. Thus, these nanocarriers offer a promising theranostic platform for simultaneous cancer treatment and monitoring.B.K. and S.A. contributed equally to this work. The authors thank King Abdullah University for Science and Technology (KAUST) for funding this work. The authors would like to thank Dr. Somayah Qutub for her valuable support and assistance in the bio experiments
Drivers of the spatiotemporal distribution of dissolved nitrous oxide and air-sea exchange in a coastal Mediterranean area
No full textAmong the well-known greenhouse gases (GHGs), nitrous oxide (N2O) is the third most impactful, possessing a global warming potential approximately 300 times greater than that of carbon dioxide (CO2) over a century. The distribution of N2O in aquatic environments exhibits notable spatial and temporal variations, and emissions remain inadequately constrained and underrepresented in global N2O emission inventories, particularly for coastal zones. This study focuses on N2O levels and air–sea fluxes in the coastal waters of the Balearic Islands Archipelago in the Western Mediterranean basin. Data were gathered between 2018 and 2023 at three coastal monitoring stations: two on the densely populated island of Mallorca and the third in the well-preserved National Park of the Cabrera Archipelago. Seawater N2O concentrations varied from 6.5 to 9.9 nmol L−1, with no significant differences being detected across the sites. When these sink–source strengths are integrated on an annual basis, the Balearic Sea is close to equilibrium with atmospheric N2O, resulting in a neutral atmosphere–ocean exchange (0.1 ± 0.2 µmolm-2d-1). A consistent seasonal pattern was noted during the study period. Machine learning analysis indicated that seawater temperature was the primary factor influencing N2O concentrations, with lesser contributions from chlorophyll levels and salinity.We express our gratitude to the Cabrera National Park staff for facilitating the work done during this study and
to the staff of the Balearic Islands Coastal Observing and Forecasting System (SOCIB) for their invaluable assistance and for the use
of their fixed station in the Bay of Palma. We thank Juan MártinezAyala (SOCIB) for their support with the sample collection and
analysis. This work contributes to CSIC Thematic Interdisciplinary
Platform PTI OCEANS+. We appreciate the AQUANITROMET
service of the Instituto Investigaciones Marinas for conducting the
methane analyses, and we thank the Agencia Estatal de Meteorología (AEMET) for providing the meteorological data.Funding for this work was provided by the
Spanish Ministry of Science (SumaEco, grant no. RTI2018–
095441-B-C21; CYCLE, grant no. PID2021-123723OB-C21,
the Government of the Balearic Islands through la Consellería
d’Innovació, Recerca i Turisme (Projecte de recerca científica i
tecnològica SEPPO, grant no. PRD2018/18), and the 2018 call
of the BBVA Foundation “Ayudas a equipos de investigación
científica” for the Posi-COIN project. Susana Flecha acknowledges
the financial support of the “Margalida Comas-2017” and “Vicenç
Munt Estabilitat-2022” postdoctoral contracts, as well as grant
no. AAEE111/2017 from the Balearic Islands Government.
Susana Flecha is staff hired under the Generation D initiative,
promoted by Red.es, an organization attached to the Ministry for
Digital Transformation and the Civil Service, for the attraction and
retention of talent through grants and training contracts financed
by the Recovery, Transformation, and Resilience Plan through the
European Union’s Next Generation funds. Mercedes de la Paz
acknowledges the financial support during the study period for the
contracts financed by the Spanish Ministry of Science under grant
nos. CTM2015–74510-JIN and PTA2019–017983-I. Fiz Fernández Pérez was supported by the FICARAM+ project (grant no.
PID2023 – 148924OB – l00)
Toward Generalizable Video Self-Supervised Learning: Benchmarking and Semantic-Motion Infused Pretraining
No full textSelf-supervised learning (SSL) has emerged as a key approach for learning video representations without human labels. However, existing methods are often evaluated in narrow settings, leaving their generalization ability unclear. We first address this gap by introducing SEVERE-benchmark++, a comprehensive benchmark that measures sensitivity across four critical factors: domain shift, sample efficiency, action granularity, and task variation. Our analysis reveals that current methods exhibit significant sensitivity across all factors, highlighting the need for more robust approaches.
To address this, we propose SMILE, a new masked video modeling framework that improves generalization by enhancing spatial and motion semantics. SMILE reconstructs CLIP-based high-level features instead of pixels and introduces synthetic object motions with trajectory-based masking to reduce temporal redundancy. Experiments across seven datasets show that SMILE achieves superior performance under various evaluation settings, offering a stronger foundation for generalizable video SSL
A Derivative-Free Algorithm for Minimization in One Dimension: Relaxation, Monte Carlo, and Sampling
No full textWe introduce a derivative-free optimization algorithm that efficiently computes minima for various classes of one-dimensional functions, including nonconvex and nonsmooth functions. This algorithm numerically approximates the gradient flow of a relaxed functional, integrating strategies such as Monte Carlo methods, rejection sampling, and adaptive techniques. These strategies enhance performance in solving a diverse range of optimization problems while significantly reducing the number of required function evaluations compared with established methods. We present a proof of the convergence of the algorithm for locally convex functions and illustrate its numerical performance by comprehensive benchmarking with test functions, showcasing different properties and characteristics. The proposed algorithm offers a substantial potential for real-world models. It is particularly advantageous in situations requiring computationally intensive objective function evaluations, such as hyperparameter tuning in machine learning or line search in large-scale optimization problems involving the discretization of partial differential equations. Funding: The research reported in this publication was supported by funding from King Abdullah University of Science and Technology (KAUST). D. A. Gomes was supported by KAUST [baseline funds and Grant KAUST OSR-CRG2021-4674]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/moor.2023.0340
Data-driven uncertainty quantification for constrained stochastic differential equations and application to solar photovoltaic power forecast data
No full textIn this work, we extend the data-driven Itô stochastic differential equation (SDE) framework for the pathwise assessment of short-term forecast errors to account for the time-dependent upper bound that naturally constrains the observable historical data and forecast. We propose a new nonlinear and time-inhomogeneous SDE model with a Jacobi-type diffusion term for the observable phenomenon of interest, simultaneously driven by the forecast and the constraining upper bound. We rigorously demonstrate the existence and uniqueness of a strong solution to the SDE model by imposing a condition for the time-varying mean-reversion parameter appearing in the drift term. After normalization, the original forecast function is thresholded to keep such time-varying mean-reversion parameters bounded. Thus, for any finite time interval, the paths of the forecast error process almost surely do not reach the time-dependent boundaries. The SDE model parameter calibration procedure is applied to user-selected approximations of the likelihood function. Another novel contribution is estimating the unknown transition density of the forecast error process with a tailored kernel smoothing technique without and with a control variate method, coupling an adequate SDE to the original one. We provide the theoretical study about how to choose the optimal bandwidth. As a case study, we fit the model to the 2019 photovoltaic (PV) solar power daily production and forecast data in Uruguay, computing the daily maximum solar PV production estimation. Two statistical versions of the constrained SDE model are fit, with the beta and truncated normal distributions as surrogates for the transition density function of the forecast error process. Empirical results include simulations of the normalized solar PV power production and pathwise confidence bands generated with the desired coverage probability through an indirect inference method. An objective comparison of optimal parametric points associated with the two selected statistical approximations is provided by applying our innovative kernel smoothing estimation technique of the transition function of the forecast error process. As a byproduct, we created a procedure providing a reliable criterion for choosing an adequate density proxy candidate that better fits the actual data at a low time cost. The methodology employs a thorough pathwise assessment of the forecast error uncertainty in situations where time-dependent boundaries and available forecasts drive the model specifications.This research was partially supported by the KAUST Office of Sponsored Research (OSR) under Award No. URF/1/2584–01–01 in the KAUST Competitive Research Grants Program Round 8, the Alexander von Humboldt Foundation, the chair Risques Financiers, Fondation du Risque, and the Laboratory of Excellence MME-DII Grant No. ANR11-LBX–0023–01 (http://labex-mme-dii.u-cergy.fr/). We thank UTE (https://portal.ute.com.uy/) for providing the data used in this research.Open access publishing provided by King Abdullah University of Science and Technology (KAUST)
Toward H <sub>2</sub> ICE: Experimental and computational characterization of hydrogen injection, mixing, and combustion
No full text
Hydrogen-fueled internal combustion engines (H
2
ICE) have great potential as future carbon-free transportation, especially in heavy-duty applications, but the implementation in real vehicles has not yet been demonstrated due to a number of technical challenges. One of the most important issues is the design of hydrogen injector systems that can provide the needed amount of fuel into the combustion chamber with rapid mixing with the main chamber gases to achieve a near homogeneous mixture, in order to ensure stable combustion without anomalies such as pre-ignition and knocking. The present study provides an overview of the ongoing FUELCOM4 project with KAUST and Saudi Aramco in an effort to enhance our knowledge of hydrogen injection, mixing, and combustion characteristics by utilizing high-fidelity laser diagnostics and simulations. First, hydrogen jet injection and mixing characteristics are investigated in a high-pressure constant volume chamber experiment with accompanying simulations for validation. Jet penetration and dispersion characteristics depending on different injector configurations are examined. Recent developments in advanced laser diagnostic techniques to quantify the hydrogen fuel distribution are also discussed. Finally, parametric studies of the effects of jet dispersion and mixing on engine combustion characteristics are presented.
<br