Browse Articles
Discover research articles across all indexed journals
Formation of a membraneless compartment regulates bacterial virulence
Optimization of pressure swing adsorption in a three-layered bed for hydrogen purification using machine learning model
A brand-new colour created by lasers, a pig-liver transplant trial gets the green light, and a nugget-sized chunk of lab-grown meat
Reaching the pinnacle of high-capacity optical transmission using a standard cladding diameter coupled-core multi-core fiber
Time-course of muscle fatigue development during intense exercise in hypoxia and normoxia
Cellular immunotherapy targeting CLL-1 for juvenile myelomonocytic leukemia
Abstract Juvenile myelomonocytic leukemia (JMML) is a myeloproliferative disorder that predominantly affects infants and young children. Hematopoietic stem cell transplantation (HSCT) is standard of care, but post-HSCT relapse is common, highlighting the need for innovative therapies. While adoptive immunotherapy with chimeric antigen receptor (CAR) T cells has improved outcomes for patients with advanced lymphoid malignancies, it has not been comprehensively evaluated in JMML. In the present study, we use bulk and single-cell RNA sequencing, mass spectrometry, and flow cytometry to identify overexpression of CLL-1 (encoded by CLEC12A) on the cell surface of cells from patients with JMML. We develop immunotherapy with CLL-1 CAR T cells (CLL1CART) for preclinical testing and report in vitro and in vivo anti-leukemia activity. Notably, CLL1CART reduce the number of leukemic stem cells and serial transplantability in vivo. These preclinical data support the development and clinical investigation of CLL-1-targeting immunotherapy in children with relapsed/refractory JMML.
An enhanced ensemble defense framework for boosting adversarial robustness of intrusion detection systems
Abstract Machine learning (ML) and deep neural networks (DNN) have emerged as powerful tools for enhancing intrusion detection systems (IDS) in cybersecurity. However, recent studies have revealed their vulnerability to adversarial attacks, where maliciously perturbed traffic samples can deceive trained DNN-based detectors, leading to incorrect classifications and compromised system integrity. While numerous defense mechanisms have been proposed to mitigate these adversarial threats, many fail to achieve a balance between robustness against adversarial attacks, maintaining high detection accuracy on clean data, and preserving the functional integrity of traffic flow features. To address these limitations, this research investigates and integrates a comprehensive ensemble of adversarial defense strategies, implemented in two key phases. During the training phase, adversarial training, label smoothing, and Gaussian augmentation are employed to enhance the model’s resilience against adversarial perturbations. Additionally, a proactive preprocessing defense strategy is deployed during the testing phase, utilizing a denoising sparse autoencoder to cleanse adversarial input samples before they are fed into the IDS classifier. Comparative evaluations demonstrate that the proposed ensemble defense framework significantly improves the adversarial robustness and classification performance of DNN-based IDS classifiers. Experimental results, validated on the CICIDS2017 and CICIDS2018 datasets, show that the proposed approach achieves aggregated prediction accuracies of 87.34% and 98.78% under majority voting and weighted average schemes, respectively. These findings underscore the effectiveness of the proposed framework in combating adversarial threats while maintaining robust detection capabilities, thereby advancing the state-of-the-art in adversarial defense for intrusion detection systems.
High entropy modulated quantum paraelectric perovskite for capacitive energy storage
Enhancing long-term adherence in elderly stroke rehabilitation through a digital health approach based on multimodal feedback and personalized intervention
Molecular basis of human GABA transporter 3 inhibition
Blockchain technology adoption in healthcare: an integrated model
Cryo-EM structure of the AAA+ SPATA5 complex and its role in human cytoplasmic pre-60S maturation
Advanced solar radiation prediction using combined satellite imagery and tabular data processing
Abstract Accurate solar radiation prediction is crucial for optimizing solar energy systems. There are two types of data that can be used to predict solar radiation, such as satellite images and tabular satellite data. This research focuses on enhancing solar radiation prediction by integrating data from two distinct sources: satellite imagery and ground-based measurements. By combining these datasets, the study improves the accuracy of solar radiation forecasts, which is crucial for renewable energy applications. This research presents a hybrid methodology to predict the solar radiation from both satellite images and satellite data. The methodology basis on two datasets; the first data set contains tabular data, and the second dataset contains satellite images. The framework divides into two paths; the first path take the input as the satellite images; this stages contains three steps; the first step is removing noise using latent diffusion model, the second step is about pixel imputation using a modified RF + Identity GAN (this model contains two modification the first modification is adding the identity block to solve mode collapse problem in the GANs and the second modification is to add the 8-connected pixel to generate a value of missing pixel near to the real missed pixel. The third step in the first path is about using the self-organizing map to identify the special informative in the satellite image. The second path take the input as tabular data and use the diffusion model to impute the missing data in the tabulated data. Finally, we merge the two path and use feature selection to be as input for the LSTM for solar radiation predictions. The experiments done prove the efficiency of the used stage such as missing pixel imputation, removing noise, missing data imputation and prediction using LSTM when compared with other available techniques. The experiments also prove the enhancement of all prediction model after adding two paths before the prediction step.
Three-dimensional genome structures of single mammalian sperm
Research on object detection and recognition in remote sensing images based on YOLOv11
Lunar primitive mantle olivine returned by Chang’e-6
Abstract The lunar mantle is important for unraveling the Moon’s formation and early differentiation processes. Here, we identify primitive lunar olivines in soils returned by the Chang’e-6 mission. These olivines have oxygen isotopic compositions plotting along the terrestrial fractionation line, and are characterized by high forsterite contents up to 95.6, and a broad range of nickel abundances from zero to 682 ppm. While the low-nickel (zero to 251 ppm), forsteritic olivines align with a Mg-suite origin, the most primitive, high-nickel olivines (337 to 682 ppm) have a different origin. They could be either the first olivine crystallized from the Lunar Magma Ocean (LMO) with an Earth-like initial composition, or crystallized from a hitherto unrecognized ultra-magnesian lava produced by extensive melting of the early LMO cumulate. The exposure of these mantle olivines was facilitated by their entrainment in ascending high-Mg lavas and conveyed to the surface at the South Pole-Aitken Basin.
Visual and refractive outcome, higher order aberrations and complications following intraocular lens exchange in eyes without comorbidities
Abstract Intraocular lens (IOL) exchange is performed to treat severe IOL-related complications. There is little published data on the impact of this procedure on the refractive outcomes and intra- and postoperative complications, especially in otherwise healthy eyes. We investigated the refractive outcome of IOL exchange surgery, including corneal aberrations; additionally, we assessed the influence of different IOL characteristics on intraoperative and postoperative complications. This prospective clinical study included 35 eyes with homogenous IOL calcification without other ocular pathologies. Using Pentacam AXL Wave (Oculus Optikgeräte GmbH, Wetzlar, Germany), corrected distance visual acuity (CDVA), target refraction compared to the postoperative spherical equivalent, corneal topography and lower and higher-order aberrations were assessed preoperatively and from 3 months after surgery. Intraoperative and postoperative complications were recorded and compared between different IOL characteristics. The secondary IOL in 53% of cases, was a retropupillary iris-fixated Artisan Aphakia (Ophtec BV, Groningen, Netherlands), 37% had a sulcus-fixated AR40e (Johnson & Johnson Vision, Irvine, USA), and 10% had a capsular bag IOL. The CDVA improved from 0.16 ± 0.14 to 0.07 ± 0.14 logMAR (p = 0.04). In most cases, the target refraction was within ± 1.0 D (Artisan: 71%, AR40e: 90%, Capsular: 100%). IOL exchange did not induce relevant change in corneal aberrations. Anterior (81%) and posterior (78%) vitrectomy were performed in most cases. The haptic design of the primary IOL did not impact intra- or postoperative complications. Although exchanging an IOL involves greater surgery compared to the initial IOL implantation, visual and refractive outcomes are good, and the exchange does not cause relevant change in aberrations. Intra- and postoperative complications are mostly mild and resolve without sequelae.
Single-Atom Saturation: A Fundamental Principle for Single-Atom-Site Catalyst Design
Border-zone cardiomyocytes and macrophages regulate extracellular matrix remodeling to promote cardiomyocyte protrusion during cardiac regeneration
Abstract Despite numerous advances in our understanding of zebrafish cardiac regeneration, an aspect that remains less studied is how regenerating cardiomyocytes invade and replace the collagen-containing injured tissue. Here, we provide an in-depth analysis of the process of cardiomyocyte invasion. We observe close interactions between protruding border-zone cardiomyocytes and macrophages, and show that macrophages are essential for extracellular matrix remodeling at the wound border zone and cardiomyocyte protrusion into the injured area. Single-cell RNA-sequencing reveals the expression of mmp14b, encoding a membrane-anchored matrix metalloproteinase, in several cell types at the border zone. Genetic mmp14b mutation leads to decreased macrophage recruitment, collagen degradation, and subsequent cardiomyocyte protrusion into injured tissue. Furthermore, cardiomyocyte-specific overexpression of mmp14b is sufficient to enhance cardiomyocyte invasion into the injured tissue and along the apical surface of the wound. Altogether, our data provide important insights into the mechanisms underlying cardiomyocyte invasion of the collagen-containing injured tissue during cardiac regeneration.