67639 research outputs found
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Higher Hölder regularity for degenerate elliptic PDEs with data in Morrey spaces
No full textWe establish sharp local tions of p C1,α regularity for weak solutions to degenerate elliptic equaLaplacian type with data in Morrey spaces. The proof relies on the FeffermanPhong inequality and standard tools from regularity theory for nonlinear PDEs.The authors thank the anonymous referee for the insightful comments and constructive suggestions that substantially improved the manuscript. This publication is based upon work supported by King Abdullah University of Science and Technology (KAUST) under Award No. ORFS-CRG12-2024-6430. GDF is part of Gruppo Nazionale per l’Analisi Matematica, la Probabilità e le loro Applicazioni (GNAMPA) - Istituto Nazionale di Alta Matematica (INdAM) and partially supported by the University of Catania, Piano della Ricerca 2016/2018 Linea di intervento 2. JMU is partially supported by UID/00324 - Centre for Mathematics of the University of Coimbra. GDF thanks KAUST for the nice hospitality and excellent research environment during his visits
Physical and functional interactions of the potassium uptake protein HAK5 and the nitrate transporter NPF6.2 is critical for the mineral nutrition of Arabidopsis
No full textABSTRACT
Potassium (K+) starvation induces the expression of gene HAK5 encoding a high-affinity K+ uptake protein, but how plants perceive the K+ status and the signaling intermediaries involved in the response remains largely unknown. To identify key regulators of K+ nutrition in Arabidopsis, a genetic screen was performed using an pHAK5:LUC reporter line, and a mutant showing stable induction of the reporter under K+-sufficient conditions was isolated. Mapping-by-sequencing identified two linked mutations affecting genes involved in K+ and nitrate nutrition, namely a loss-of-function in the K+ uptake channel AKT1 and a gain-of-function allele of the nitrate transporter NPF6.2/NRT1.4 (NPF6.2V210M) that doubled the rate of nitrate transport. We report that the physical interaction of NPF6.2 and HAK5 transport proteins resulted in reciprocal interference. Co-expression in Xenopus oocytes of NPF6.2 with the regulatory kinase CIPK23 or the mutant protein NPF6.2V210M alone inhibited HAK5 transport, whereas HAK5 inhibited nitrate transport by NPF6.2 and NPF6.2V210M. We conclude that mutation NPF6.2V210M enhanced the nutritional defects associated to the loss of AKT1 function through the inhibition of HAK5. These findings evidence an intimate molecular crosstalk between transporters involved in the mineral nutrition of plants. The mutual interference when both transport systems are operative may represent a novel integrative regulatory mechanism in mineral nutrition.<br
Digital Twins: Initiatives, Technologies, and Use Cases in the Arab World
No full textDigital twins (DTs) are virtual replicas of components, assets, systems, or processes, linked to their real-world counterparts, continuously updating their states and simulating their behavior in real time, as illustrated in Figure 1. They are adopted for monitoring, predicting, and optimizing the performance of diverse systems, bridging the gap between design, testing and deployment. Through conceptual design, virtual verification, and commissioning, DTs open ample room for data-driven optimization and decision making, enhancing strategic planning and risk mitigation in verticals of paramount importance to the Arab world’s economic development and citizens’ safety and well-being
Motion Tracking for Automatically Controlled Functional Electrical Stimulation System in Mirror Therapy Configuration: the Enhanced Lazarus Solution
No full textIn this work, we present the potential application of an automatic control system for adjusting the parameter values of Functional Electrical Stimulation (FES). FES is a key enabling technology in the functional recovery of patients undergoing neuro-muscular rehabilitation. In this application, successful stimulation outcomes depend on the accurate setting of patient-specific parameters – such as pulse width and stimulation frequency – which are typically determined by physiotherapists based on their clinical experience. The proposed system is implemented in a Mirror Therapy setup, where an injured limb is controlled via FES to replicate the movement of a healthy limb – either belonging to the same patient or to a physiotherapist. The FES activation signal is derived from surface Electromyography (sEMG) recordings of the healthy limb and transmitted to the FES unit after compression using an Average Threshold Crossing (ATC) algorithm. Optimal FES parameters are adjusted through a closed-loop feedback system based on motion tracking of the injured limb, which is continuously compared to the movement of the healthy limb. Additionally, we explore the possibility of replacing the ATC algorithm used for compressing the sEMG signal from the healthy limb with a Compressed Sensing-based approach. This alternative would enable full reconstruction of the sEMG signal – unlike ATC – at the cost of only a minimal increase in computational effort. A prototype was tested on 22 healthy subjects under five different parameter configurations, confirming the feasibility and adaptability of the proposed system
Impact of soil moisture and rainfall variability on soybean crop yield during El Niño episodes in Maharashtra, India
No full textThis study presents a comprehensive analysis of the influence of ENSO events on soil moisture, rainfall, and soybean crop yields, alongside an evaluation of trends and their correlations with soybean crop yield. GLDAS soil moisture data were retrieved, pre-processed, and agriculturally masked using Google Earth Engine (GEE), while IMD rainfall data were processed locally and integrated for zonal statistics in Marathwada and Vidarbha. Trends for 2003–2022 were assessed using the Mann-Kendall test. Marathwada and Vidarbha regions, contributing 39% of India’s soybean production, were found to be highly susceptible to El Niño events especially western and central parts of this region. El Niño negatively affected crop yield, with reductions ranging from − 12 to -70%, including − 7 to -40% rainfall decreases, -24% to -27% number of rainy days decreases and − 2 to -6% lower soil moisture. During a very strong El Niño year, Marathwada experienced a 40% rainfall deficit, 24% reduction in rainy days and a 70% crop yield decline, while Vidarbha had a 7% rainfall deficit, 27% reduction in rainy days and a 57% crop yield reduction. Soil moisture trends indicated declining levels in Vidarbha during critical soybean growth months (June and August), whereas Marathwada showed no significant trend. Rainfall trends revealed an increase in September in Marathwada, affecting crop maturity, while Vidarbha had a beneficial rainfall trend in July, promoting crop growth and soil moisture. Correlations between soil moisture, rainfall, and soybean yield varied, with Marathwada exhibiting correlations of 0.58 for soil moisture, 0.56 for rainfall, and 0.8 for rainy days, and Vidarbha displaying a correlation of 0.29 for soil moisture, 0.53 for rainfall, and 0.66 for rainy days.This research received no external funding
TiNbC MXene cathode for high-performance aluminum-ion batteries
No full textAl-ion batteries (AIBs) have emerged as a promising energy storage technology due to their high theoretical capacity, cost-effectiveness, and superior safety. However, the lack of stable and efficient cathode materials capable of reversible Al-complex ion (e.g., [AlCl4]−) insertion/extraction remains a critical challenge. In this work, we developed TiNbCTx MXene as a high-performance cathode material for AIBs, achieving remarkable capacity and cycling stability. Unlike symmetric-structured Ti2CTx, the TiNbCTx cathode leverages synergistic Ti–Nb bimetallic effects to enhance the electronic conductivity and electrochemical activity. Here we show, TiNbCTx delivers a high reversible capacity of 194 mAh·g−1 at 0.2 A·g−1 with 800-cycle stability. Through combined experimental characterization and density functional theory (DFT) calculations, we elucidate the kinetic mechanisms of energy storage, offering fundamental insights for the rational design of advanced cathode materials in AIBs.S.T. acknowledges the financial support from the National Natural Science Foundation of China (Grant number 52203092) and the Natural Science Foundation of Jiangxi Province, China (Grant No. 20232BAB204026). We thank for the resources from KAUST
Accelerated Spatio-Temporal Bayesian Modeling for Multivariate Gaussian Processes
No full textMultivariate Gaussian processes (GPs) offer a powerful probabilistic framework to represent complex interdependent phenomena. They pose, however, significant computational challenges in high-dimensional settings, which frequently arise in spatial-temporal applications. We present DALIA, a highly scalable framework for performing Bayesian inference tasks on spatio-temporal multivariate GPs, based on the methodology of integrated nested Laplace approximations. Our approach relies on a sparse inverse covariance matrix formulation of the GP, puts forward a GPU-accelerated block-dense approach, and introduces a hierarchical, triple-layer, distributed memory parallel scheme. We showcase weak scaling performance surpassing the state-of-the-art by two orders of magnitude on a model whose parameter space is 8 larger and measure strong scaling speedups of three orders of magnitude when running on 496 GH200 superchips on the Alps supercomputer. Applying DALIA to air pollution data from northern Italy over 48 days, we showcase refined spatial resolutions over the aggregated pollutant measurements.This work was supported by the Swiss National Science Foundation (SNSF) under grant n○209358 (QuaTrEx), and by
the Platform for Advanced Scientific Computing in Switzerland (BoostQT). We acknowledge the scientific support and HPC resources from CSCS under projects sm96 and lp16 as well as the Erlangen National High Performance Computing Center (NHR@FAU) of the Friedrich-Alexander-Universitat¨ Erlangen-Nurnberg (FAU) under the NHR project 80227
Topological Analysis of Seizure-Induced Changes in Brain Hierarchy Through Effective Connectivity
No full textTraditional Topological Data Analysis (TDA) methods, such as Persistent Homology (PH), rely on distance measures (e.g., cross-correlation, partial correlation, coherence, and partial coherence) that are symmetric by definition. While useful for studying topological patterns in functional brain connectivity, the main limitation of these methods is their inability to capture the directional dynamics - which are crucial for understanding effective brain connectivity. We propose the Causality-Based Topological Ranking (CBTR) method, which integrates Causal Inference (CI) to assess effective brain connectivity with Hodge Decomposition (HD) to rank brain regions based on their mutual influence. Our simulations confirm that the CBTR method accurately and consistently identifies hierarchical structures in multivariate time series data. Moreover, this method effectively identifies brain regions showing the most significant interaction changes with other regions during seizures using electroencephalogram (EEG) data. These results provide novel insights into the brain’s hierarchical organization and illuminate the impact of seizures on its dynamics
Methicillin-resistant Staphylococcus aureus in Saudi Arabia: genomic evidence of recent clonal expansion and plasmid-driven resistance dissemination
No full textObjectives: Staphylococcus aureus is a leading cause of hospital-acquired infections worldwide. Over recent decades, methicillin-resistant Staphylococcus aureus (MRSA), which is resistant to multiple antimicrobials, has emerged as a significant pathogenic strain in both hospital and community settings. The rapid emergence and dissemination of MRSA clones are driven by a dynamic and evolving population, spreading swiftly across regions on epidemiological time scales. Despite the vast geographical expanse and diverse demographics of the Kingdom of Saudi Arabia and the broader West Asia region, the population diversity of MRSA in hospitals in these areas remains underexplored.
Methods: We conducted a large-scale genomic analysis of a systematic Staphylococcus aureus collection obtained from 34 hospitals across all provinces of KSA, from diverse body sites between 2022 and 2024. The dataset comprised 581 MRSA and 31 methicillin-susceptible Staphylococcus aureus (MSSA) isolates, all subjected to whole-genome sequencing. A combination of phylogenetic and population genomics approaches was utilized to analyze the genomic data. Hybrid sequencing approach was employed to retrieve the complete plasmid content.
Results: The population displayed remarkable diversity, comprising 48 distinct sequence types (STs), with the majority harboring community-associated SCCmec loci (types IVa, V/VII, and VI). Virulence factors associated with community-acquired MRSA (CA-MRSA), including Panton-Valentine Leukocidin (PVL) genes, were identified in 12 distinct STs. Dominant clones, including ST8-t008 (USA300), ST88-t690, ST672-t3841, ST6-t304, and ST5-t311, were associated with infections at various body sites and were widely disseminated across the country. Linezolid and vancomycin resistance were mediated by cfr-carrying plasmids and mutations in the vraR gene (involved in cell-wall stress response) and the murF gene (involved in peptidoglycan biosynthesis) in five isolates, respectively. Phylodynamic analysis revealed rapid expansion of the dominant clones, with their emergence estimated to have occurred 10–20 years ago. Plasmidome analysis uncovered a diverse repertoire of blaZ-containing plasmids and the sharing of erm(C)-encoding plasmids among major clades. The acquisition of plasmids coincided with clonal expansion.
Conclusions: Our results highlight the recent concurrent expansion and geographical dissemination of CA-MRSA clones across hospitals. These findings also underscore the interplay between clonal spread and horizontal gene transfer in shaping the resistance landscape of MRSA.The author(s) declare that financial support was received for the research and/or publication of this article. GZ, HH, JH, OF, RH, SI, MM, and DM were supported by the KAUST faculty baseline fund (BAS/1/1108-01-01). AP was supported by the KAUST baseline (BAS/1/1020-01-01). GZ, HH, JH, OF, RH, SI, MM, and DM were also supported by FCC/1/5932-01-03 from the KAUST Center of Excellence for Smart Health. The authors extend their appreciation to the Deputyship for Research and Innovation, Ministry of Education in Saudi Arabia for funding this research (IFKSUOR3-478)
Improving differentiable hydrologic modeling with interpretable forcing fusion
No full textAtmospheric forcings for hydrologic models often contain significant errors, but traditional modifications only employ bias correction or distributional transformations based on rainfall measurements. Deep learning could fuse multiple datasets for improved hydrologic modeling, but is difficult to interpret. Here we introduce a “differentiable” data fusion framework where a neural network is pre-trained to provide parameters a process-based hydrologic model while a second network is trained to weigh multiple forcings (Daymet, NLDAS, and Maurer) for a fused precipitation input to the combined model. The fused precipitation data greatly improved streamflow simulation performance (both low flow and high flow, but especially high flow). Applying adaptive weights to a single forcing did not yield improvements. Overall, the fusion placed a higher weight on Daymet, and slightly lower weights on NLDAS and Maurer. NLDAS’s weights increased in the humid eastern US while Maurer’s increased in mountainous regions. The fused precipitation had similar means and large-magnitude event performance to Daymet. However, it exhibited higher correlation with station-based precipitation than any individual forcing or their simple average, and had close to the smallest bias for large storms. Pre-training the parameterization network based on the best-performing single forcing (Daymet) yielded better results than those based on the average of forcings. Overall, the differentiable hydrologic model offers a generic hydrology-informed fusion method to improve streamflow prediction.This work was mainly supported by the U.S. Department of Energy under award DE-SC0016605. MP was supported by Federal Award Identification W912HZ-19-2-0023 “Research to Continue Investigation of Atmospheric Rivers (AR) and the Application of Using AR Forecast Capabilities to Inform Reservoir Operations within the USACE”. YS, KL, and CS were also supported by Cooperative Institute for Research to Operations in Hydrology (CIROH) through the NOAA Cooperative Agreement with University of Alabama (grant no. NA22NWS4320003) under subaward A22-0307-S003. The statements, findings, conclusions, and recommendations are those of the authors and do not necessarily reflect the view of NOAA. Computation was partially supported by the National Energy Research Scientific Computing Center (NERSC), a U.S. Department of Energy Office of Science User Facility located at Lawrence Berkeley National Laboratory, operated under Contract No. DE-AC02-05CH11231 using NERSC award ERCAP0024296