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A Novel KDF1 Variant is Associated With Multiple Natal Teeth, Tooth Agenesis, and Root Maldevelopment.
No full textObjectiveNatal teeth are teeth that are present at birth. Multiple natal teeth are extremely rare. The objective of this study was to find the molecular aetiology of a unique dental phenotype including natal teeth, tooth agenesis, and root maldevelopment in a 5-generation family.MethodsOral and radiographic examination, linkage analysis, whole genome sequencing, and an immunohistochemical study of Kdf1 during tooth development in the mouse embryo were performed. A protein model was generated.ResultsWe report a 5-generation family in which multiple natal teeth, oligodontia, and root maldevelopment manifested with autosomal dominant inheritance. Linkage analysis and whole genome sequencing revealed a novel pathogenic variant c.845T>G; p.Ile282Ser, which cosegregated in 9 affected and 10 unaffected family members. This amino acid Ile282 is highly conserved and is important for the stabilization of a small helical fragment. This stabilization is lost in the Ile282Ser mutant, resulting in disruption of the interaction of KDF1 with its partner proteins, including IKKA, which are important for epidermal proliferation and differentiation and subsequent tooth development.ConclusionsOur study demonstrates for the first time that natal teeth, tooth agenesis, and root maldevelopment are caused by a KDF1 variant. Our study highlights the important role of KDF1 in tooth formation and eruption.We thank our patients and their families for their kind cooperation and for giving us the consent to use clinical pictures and the medical and dental information of the patient for the benefit of other patients. We are indebted to Dr Kent Taylor of LA Biomed at Harbor-UCLA Medical Center in Torrance CA for performing the linkage analysis. Vince Funari of Cedars-Sinai Medical Center extracted DNA for the linkage study. Ophir Klein MD, PhD of Cedars-Sinai Medical Center also provided useful advice concerning this project.This research was supported by Chiang Mai University (P.K.) and The Genomics Thailand Research Grant of the Health System Research Institute of Thailand (64-123) (P.K.). The research by STA was supported by funding from King Abdullah University of Science and Technology (KAUST) – 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 at KAUST (S.T.A.)
Enhancing multiparameter elastic full-waveform inversion with a Siamese network
No full textUsing the proper data misfit measure within full-waveform inversion (FWI) is crucial for achieving robust inversion performance. This choice is complicated by the fact that our simulations are often based on simplified assumptions of the earth admitting clean waveforms that differ from those recorded. Thus, we extend the Siamese framework for multiparameter elastic full-waveform inversion (EFWI). For this elastic implementation, we use wavelet transforms to help the network recognize the features of the data it needs to highlight for the misfit measure. Specifically, we transform seismic data into the wavelet domain using the Haar mother wavelet, with the approximation and detail components serving as inputs to the Siamese network. The Siamese network comprises two identical convolutional neural network branches with shared weights. They map the wavelet coefficients to values that can provide improved data comparison, using the Euclidean distance to measure the loss between the Siamese output branches. This proposed Siamese network is a self-supervised deep learning model, where its parameters are optimized during the EFWI iterative process. A skip connection between the Siamese network's input and output is used to ensure stability, given the random initialization of the Siamese network parameters. This design ensures that, during the initial iterations, the framework behaves similarly to conventional EFWI, preventing the introduction of noise or instability. After a few iterations, the Siamese network learns to extract significant features from its input data, leading to robust inversion performance. The integration of the Siamese network incurs only minimal additional cost compared to traditional EFWI. We tested the framework on synthetic and real data examples, demonstrating its ability to enhance the inversion performance of the conventional EFWI.This publication is based on work supported by the King Abdullah University of Science and Technology (KAUST). The authors thank the DeepWave Consortium sponsors for their support. Special thanks to Mohammad H. Taufik, Mustafa Alfarhan, and Matteo Ravasi for their assistance in preparing the Volve field data
CD34: A Key Modulator of Cytoskeletal Remodeling and E-selectin Ligand Organization in Blood Stem Cells
No full textAcute myeloid leukemia (AML) is the most common aggressive type of leukemia in adults. It originates in immature white blood cells in the bone marrow and can spread into the bloodstream, and migrate to secondary sites such as the brain, liver, and the spleen. Recent research from our lab has demonstrated that AML cell migration is partly regulated by the interaction between CD34, a hematopoietic stem cell marker and surface glycoprotein expressed on AML cells, and its corresponding receptor, E-selectin, expressed on tissue cells that AML cells migrate towards. Our findings further showed that CD34 binding to E-selectin stimulates the phosphorylation of ezrin at the Thr-567 site, enabling ezrin to link actin filaments to the plasma membrane and potentially promote the formation of membrane projection (i.e.,
microvilli)—key structures in cell motility. Given the crucial role of Rho-associated coiled-coil kinase (ROCK) in regulating actin dynamics and microvilli formation, we sought to investigate its involvement in the ezrin signaling pathway. Treatment with the ROCK inhibitor (Y-27632) led to a significant reduction in ezrin phosphorylation at Thr-567, suggesting a disruption in the linkage between actin and the plasma membrane. In vitro rolling assay demonstrated that inhibiting ROCK consequently impaired cell migration due to altered microvilli formation.
Beyond its effect on ezrin, CD34 was also suspected to influence the localization and spatial organization of other E-selectin ligands, including PSGL-1, CD44, CD43, during cell interaction with E-selectin under flow conditions. Using a microfluidics-based chamber assay coupled with super-resolution imaging (TIRF and STORM), we observed notable changes in the clustering patterns of these E-selectin ligands and ezrin before and after CD34 knockdown. Collectively, our findings highlight the potential role of ROCK in CD34 downstream signaling and underscore the regulatory function of CD34 in orchestrating the spatial organization of other E-selectin ligands—effects that are critical to the process of cellular migration
ResidualViT for Efficient Temporally Dense Video Encoding
No full textSeveral video understanding tasks, such as natural language temporal video grounding, temporal activity localization, and audio description generation, require "temporally dense" reasoning over frames sampled at high temporal resolution. However, computing frame-level features for these tasks is computationally expensive given the temporal resolution requirements. In this paper, we make three contributions to reduce the cost of computing features for temporally dense tasks. First, we introduce a vision transformer (ViT) architecture, dubbed ResidualViT, that leverages the large temporal redundancy in videos to efficiently compute temporally dense frame-level features. Our architecture incorporates (i) learnable residual connections that ensure temporal consistency across consecutive frames and (ii) a token reduction module that enhances processing speed by selectively discarding temporally redundant information while reusing weights of a pretrained foundation model. Second, we propose a lightweight distillation strategy to approximate the frame-level features of the original foundation model. Finally, we evaluate our approach across four tasks and five datasets, in both zero-shot and fully supervised settings, demonstrating significant reductions in computational cost (up to 60%) and improvements in inference speed (up to 2.5x faster), all while closely approximating the accuracy of the original foundation model.Their search reported in this publication was supported by funding from King Abdullah University of Science and Technology (KAUST)-Center of Excellence for Generative AI, under award number 5940
Symbiotic plant-bacterial-fungal interaction orchestrates ethylene and auxin signaling for optimized plant growth.
No full textThe complex and mutual interactions between plants and their associated microbiota are key for plant survival and fitness. From the myriad of microbes that exist in the soil, plants dynamically engineer their surrounding microbiome in response to varying environmental and nutrient conditions. The notion that the rhizosphere bacterial and fungal community acts in harmony with plants is widely acknowledged, yet little is known about how these microorganisms interact with each other and their host plants. Here, we explored the interaction of two well-studied plant beneficial endophytes, Enterobacter sp. SA187 and the fungus Serendipita indica. We show that these microbes show inhibitory growth in vitro but act in a mutually positive manner in the presence of Arabidopsis as a plant host. Although both microbes can promote plant salinity tolerance, plant resilience is enhanced in the ternary interaction, revealing that the host plant has the ability to positively orchestrate the interactions between microbes to everyone's benefit. In conclusion, this study advances our understanding of plant-microbiome interaction beyond individual plant-microbe relationships, unveiling a new layer of complexity in how plants manage microbial communities for optimal growth and stress resistance.This work is a part of the DARWIN21 project (http://www.darwin21.org). We are thankful to Prof. Ralf Oelmüller, Friedrich Schiller University – Jena, for kindly sharing the S. indica strain with us. We thank Dr. Ikram Blilou for providing the seeds of auxin mutants. We thank Dr. Katja Froehlich and Dr. Sabiha Parween for their technical support, and all members of the Hirt lab for useful discussions. The authors are grateful to the KAUST growth facility and Analytical Core Lab for their technical support. This work was supported by KAUST funding BAS/1/1062-01-01
Conditional Diffusion Model for Robust 3-D Microseismic Event Localization from Noisy Waveforms
No full textLocating microseismic events is crucial to monitoring fracking activities, CO2 injection, and reservoirs in general. However, the process of locating such events is challenging, especially in low signal-to-noise scenarios. Thus, we introduce a conditional diffusion model for direct inversion of 3-D microseismic event locations from relatively irregularly spaced noisy waveform gathers, eliminating the need for manual arrival-time picking and reducing sensitivity to noise and velocity model uncertainty. The influence of the unknown event origin time on the waveform gathers is eliminated by cross-correlating every trace from the gather with a reference trace from the same gather. The 3-D microseismic event location coordinates (x, y, z) are projected onto two 2-D (X-Z and Y-Z) Gaussian heatmaps, enabling the use of a 2-D conditional diffusion model for the inversion task in contrast to traditional localization techniques. The model is trained to generate accurate microseismic event locations conditioned on microseismic waveform gathers. Tests on semi-synthetic data from the ToC2ME project demonstrate the model’s accuracy, robustness to noise, and computational efficiency. Furthermore, results from a real field microseismic event indicate high stability in the predicted event coordinates. These findings confirm the feasibility of using conditional diffusion models for reliable field-scale event localization.The authors thank Matteo Ravasi for insightful discussions, and KAUST and the sponsors of the DeepWave Consortium for supporting this research. This research used the resources of the Supercomputing Laboratory at King Abdullah University of Science and Technology(KAUST) in Thuwal, Saudi Arabia
Enhancing the Performance of InGaN Long Wavelength Micro-LEDs Toward AR/VR Applications: Sidewall Passivation and Light Extraction Improvement
No full textInGaN-based light emitters are promising technology for next-generation display and artificial reality (AR)/virtual reality (VR) applications, offering high resolution, brightness, and stability. To meet industrial requirements, further performance enhancements are essential. As such, this thesis explores two key approaches: first, minimizing sidewall defects caused by harsh dry etching processes, and second, enhancing light extraction efficiency (LEE) through the use of transparent p-electrodes.
A selective passivation technique for p-GaN using hydrogen plasma treatment is demonstrated to enhance the performance of InGaN single quantum well (SQW) red light-emitting diodes (LEDs). Insulating regions are created on the p-GaN surface, suppressing current injection beneath the p-pad and along the mesa edges, thereby allowing improved light output and reduced non-radiative recombination. Additionally, the temperature dependence of InGaN SQW red LEDs is explored in comparison to AlGaInP counterparts, revealing superior thermal stability and minimal wavelength shifts in InGaN LEDs.
Further analysis focuses on improvement in LEE for micro-LEDs. Transparent indium tin oxide (ITO) is introduced as p- and n-electrode to replace traditional opaque metal electrodes in InGaN green and red micro-LEDs, aiming to enhance light output. ITO electrodes exhibit high transmittance and low resistivity, whereas metal electrodes have low transmittance and significant absorption. ITO electrode thickness is optimized. An enhancement in LEE for InGaN red and green micro-LEDs operating at low current injections is demonstrated. The performance of micro-LEDs with ITO electrodes is compared to those with conventional metal electrodes, showing improved external quantum efficiency (EQE) and wall-plug efficiency (WPE). These findings provide valuable insights for developing high-performance micro-LEDs in high-definition display and AR/VR applications
A Dual-Gradient Patterned Current Collector with Built-In Stress Relief for Stable Li Metal Anodes
No full textConstructing 3D Cu-based current collectors (CCs) is a promising strategy to stabilize Li metal anodes. However, the intrinsic lithiophobic nature of Cu hinders uniform Li diffusion and induces inhomogeneous Li deposition, whereas the insufficient understanding of stress evolution during Li deposition limits insights into its role in dendrite formation. Herein, a dual-gradient patterned Cu-Ag CC (PCA-CC) is designed with spatially ordered microstructures. The patterned architecture increases the electrode–electrolyte contact area and redistributes local current density through regularly aligned surface microgrooves. A gradient in lithiophilicity and conductivity directs Li nucleation, promoting uniform deposition and improved cycling stability. In addition, the ordered microgrooves provide a pathway for stress relaxation during Li plating, which helps suppress dendrite formation. As a result, the PCA-CC enables stable and durable electrochemical performance. Li/PCA-CC symmetric cells achieve long-term cycling for over 900 h at 1 mA cm−2 and 1 mAh cm−2. Furthermore, Li/PCA-CC | LFP full cells demonstrate excellent capacity retention and rate capability, maintaining stability across a wide range of rates. This study presents a scalable dual-gradient CC that integrates structural design, surface chemistry, and stress regulation to advance safer and high-performance Li metal batteries.This work was financially supported by National Natural Science Foundation of China (52472214, 22409095, and 22201135), Natural Science Foundation of Jiangsu Province of China (BK20230368 and BK20220385), Funded by Basic Research Program of Jiangsu (BK20243057), Science and Technology Program of Suzhou (SYG202354), Nanjing University of Posts and Telecommunications Start-up Fund (NY223099, NY223054, and NY222094)
Epigenetic Age Prediction Remains Stable Across Common Variants and Diverse Ancestries
No full textEpigenetic clocks, based on DNA methylation profiles at CpG sites, are widely recognized as reliable biomarkers of biological aging. However, common single-nucleotide polymorphisms (cSNPs), genomic variants that can overlap CpG sites, may affect DNA methylation profiles in ways that potentially interfere with the accuracy of epigenetic clocks. Moreover, because the prevalence of cSNPs varies across populations, such cSNP-CpG overlaps may differentially affect the age predictions of epigenetic clocks in diverse cohorts. Here, we present the first systematic cross-ancestry evaluation of cSNP robustness in the epigenetic clock, examining how cSNP-CpG overlaps affect the performance of epigenetic clocks across nine major genomic ancestry groups. We employed three complementary strategies: (a) testing whether cSNP-CpG overlaps are overrepresented in established epigenetic clocks or particular populations, (b) evaluating whether overlapping CpG sites correspond to the most influential aging predictors within clock models, and (c) simulating the effects of cSNP-associated methylation changes on predicted biological age. Our findings indicate that cSNP-CpG overlaps are not enriched among the CpG sites used in current epigenetic clocks, nor do they tend to involve the most influential sites. Furthermore, our simulation analysis revealed that current epigenetic clocks appear robust to cSNP-related methylation variations. Our findings underscore the overall stability of current epigenetic clocks, even in the presence of population-specific cSNP-CpG overlaps that are known to affect DNA methylation levels.The authors thank King Abdullah University for Science & Technology (KAUST) Supercomputing Laboratory for providing computational resources essential for the analysis
Demystifying Misconceptions in Social Bots Research
No full textResearch on social bots aims at advancing knowledge and providing solutions to one of the most debated forms of online manipulation. Yet, social bot research is plagued by widespread biases, hyped results, and misconceptions that set the stage for ambiguities, unrealistic expectations, and seemingly irreconcilable findings. Overcoming such issues is instrumental toward ensuring reliable solutions and reaffirming the validity of the scientific method. Here, we discuss a broad set of consequential methodological and conceptual issues that affect current social bots research, illustrating each with examples drawn from recent studies. More importantly, we demystify common misconceptions, addressing fundamental points on how social bots research is discussed. Our analysis surfaces the need to discuss research about online disinformation and manipulation in a rigorous, unbiased, and responsible way. This article bolsters such effort by identifying and refuting common fallacious arguments used by both proponents and opponents of social bots research, as well as providing directions toward sound methodologies for future research.Marinella Petrocchi and Angelo Spognardi are supported by project SERICS (PE00000014) under the MUR National Recovery and Resilience Plan funded by the European Union - NextGenerationEU.
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Stefano Cresci is supported by the European Union – Next Generation EU, Mission 4 Component 1, for project PIANO (CUP B53D2301 3290006) and by the ERC project DEDUCE under grant #101113826