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Ultimate bearing capacity of embedded strip footings on rock slopes under inclined loading conditions using adaptive finite element limit analysis
A unified approach for weakly supervised crack detection via affine transformation and pseudo label refinement
Optimal satellite selection using quantum convolutional autoencoder for low-cost GNSS receiver applications
Abstract The increasing reliance on global navigation satellite systems for diverse applications necessitates the development of efficient satellite selection methods to optimize positioning accuracy and system performance. In particular, low-cost global navigation satellite systems receivers face challenges in managing data from multiple visible satellites, often resulting in suboptimal performance due to high geometric dilution of precision values. Effective satellite selection is crucial for improving the accuracy and reliability of positioning solutions in these systems. Quantum computing and machine learning provide promising solutions by using data patterns for complex optimization problems. This work proposes the quantum convolutional autoencoder-based optimal satellite selection method. This new satellite selection method examined the data collected from the receiver located at latitude 16.33° N and longitude 80.62° E, collected on March 10, 2022. The main aim is to enhance the performance of low-cost receivers by minimizing the geometric dilution of precision values and optimizing the tetrahedron volume function. Quantum convolutional autoencoders process the satellite data to balance the navigational solution’s computational burden and the navigational algorithm’s accuracy. The model aims to identify the most optimal satellites for positioning by setting geometric dilution of precision as the cost function. The QCAE-based method achieves a CEP of 1.384 m and SEP of 1.759 m for four selected satellites, compared to 5.937 m and 6.691 m for PSOSSM. For nine satellites, QCAE achieves a CEP of 1.287 m and SEP of 1.713 m, while PSOSSM results in 5.725 m and 6.385 m, respectively. Additionally, QCAE reduces computations by over 64%, requiring 730 multiplications and 713 additions, compared to 2034 multiplications and 2017 additions for all visible satellites. This proposed approach provides the optimal navigation solution for cost-effective implementations in a real-time environment. This research provides new insights into satellite selection strategies using machine learning approaches.
Research on optimization strategies of university ideological and political parenting models under the empowerment of digital intelligence
Identification of potent multi-target antiviral natural compounds from the fungal metabolites against aspartyl viral polymerases
Navigating biodiversity patterns in fragmented seagrass mosaics
Graphene quantum dot-coated polystyrene microsphere multilayer colloidal crystals with distributed Bragg reflector absorption
Graph attention and Kolmogorov–Arnold network based smart grids intrusion detection
Toward a modeling and analysis method of cyber-physical systems architecture evolution based on bigraph
Loss of ARID1A leads to a cold tumor phenotype via suppression of IFNγ signaling
Abstract The collapse of inflammatory signaling that recruits cytotoxic immune cells to the tumor microenvironment contributes to the immunologically cold tumor phenotype in neuroblastoma (NB) and is a barrier to NB immunotherapy. Multiple studies have reported that MYCN amplification, a trait of high-risk NB, correlates with a loss of inflammatory signaling; but MYCN also correlates with 1p36 deletions in NB where the SWI/SNF chromatin remodeling complex subunit ARID1A (1p36.11) is located. ARID1A is known to support inflammatory signaling in adult cancers but its role in NB inflammatory signaling is unexplored. We find MYCN overexpression causes a stronger inflammatory response to interferon-gamma (IFNγ). ARID1A knockdown causes a weaker inflammatory response and reduces IFNγ induced gene signatures for the transcription factor interferon response factor 1 ( IRF1 ). We found ARID1A is a functional interactor of IRF1 by co-immunoprecipitation studies, and ARID1A silencing causes loss of activating chromatin marks at the IRF1 target gene CXCL10 . We model that IRF1 uses ARID1A containing SWI/SNF to promote CXCL10 in response to IFNγ. Our work clarifies that the loss of ARID1A, which tightly associates with MYCN amplification, causes reduced inflammatory signaling. This work finds that ARID1A is a critical regulator of inflammatory signaling in NB and provides rationale for testing immune therapies in MYCN amplified NB that are effective in adult ARID1A mutated cancers.
Structures of respiratory syncytial virus G bound to broadly reactive antibodies provide insights into vaccine design
Abstract Respiratory syncytial virus (RSV) is a leading cause of severe lower respiratory tract disease in infants and older adults. The attachment glycoprotein (RSV G) binds to the chemokine receptor CX3CR1 to promote viral entry and modulate host immunity. Antibodies against RSV G are a known correlate of protection. Previously, several broadly reactive, high-affinity anti-RSV G human monoclonal antibodies were isolated from RSV-exposed individuals and were shown to be protective in vitro and in vivo. Here, we determined the structures of three of these antibodies in complex with RSV G and defined distinct conformational epitopes comprised of highly conserved RSV G residues. Binding competition and structural studies demonstrated that this highly conserved region displays two non-overlapping antigenic sites. Analyses of anti-RSV G antibody sequences reveal that antigenic site flexibility may promote the elicitation of diverse antibody germlines. Together, these findings provide a foundation for next-generation RSV prophylactics, and they expand concepts in vaccine design for the elicitation of germline lineage-diverse, broadly reactive, high-affinity antibodies.
Preclinical evaluation of a novel antibody–drug conjugate OBI-992 for Cancer therapy
Aluminum doped zinc oxide nanoplatelets based sensor with enhanced hydrogen sulfide detection
Zein–sodium caseinate–diosmin nanoparticles as a promising anti-cancer agent with targeted efficacy against A2780 cell line
Machine learning reveals glycolytic key gene in gastric cancer prognosis
Psychopathic personality traits are associated with experimentally induced approach and appraisal of fear-evoking stimuli indicating fear enjoyment
Abstract The extent to which deficits in the perception and experience of fear contribute to psychopathic symptoms is an ongoing matter of debate. Traditional theories emphasize diminished threat processing as the core fear deficit in psychopathic individuals, whereas recent approaches, such as the fear enjoyment hypothesis (FEH), propose that anomalies in the subjective experience of fear are related to interpersonal-affective psychopathic traits. In order to test predictions of the FEH, we conducted an online experimental study to examine whether approach and avoidance tendencies in response to fear-eliciting stimuli are related to specific psychopathic traits (interpersonal-affective symptoms and boldness). We expected these traits to be associated with faster approach towards and slower avoidance of fear-evoking images in an approach-avoidance task (AAT). In a community-based sample of 211 individuals (69% female), self-reported interpersonal-affective traits predicted more positive appraisal ratings of and a faster approach towards fear-evoking stimuli in the AAT paradigm. The link between psychopathic traits and faster approach towards fear images could not be explained by traits related to impulsiveness or impaired inhibitory control. The present findings shed light on the subjective appraisal of fear-evoking situations by individuals with elevated psychopathic traits. Implications on etiological models of psychopathy are discussed.
Early-life gut microbiome is associated with behavioral disorders in the Rio birth cohort
Insight into the inhibitory activity of mangiferin and Silybin against HER2 and EGFR using theoretical and experimental approaches
Abstract Despite the advances made in diagnosing and treating breast cancer, it continues to pose a significant threat to women’s health. High-risk mutations can lead to high resistance to current treatments and poor prognosis. Therefore, new treatment strategies are needed. Mangiferin and silybin, two natural substances obtained from plants, have demonstrated encouraging results as anticancer drugs. This study investigated the activity of these compounds against two therapeutic targets, human epidermal growth factor receptor 2 (HER2) and epidermal growth factor receptor (EGFR). We assessed the binding affinity and stability of these compounds with the active sites of wild-type and mutated HER2 and EGFR by using computational screening techniques, namely molecular docking, density functional theory, and molecular dynamics (MD) simulations with the MMGBSA method. We used molecular docking, triplicate MD simulations summing 300 ns each, and density functional theory analysis to estimate the binding mechanism of mangiferin and silybin inside the wild-type and mutated EGFR and HER2 active regions. Moreover, an in vitro experiment showed that mangiferin and silybin inhibited the growth of two HER2-positive breast cancer cell lines, BT-474 and SK-BR-3, at micromolar concentrations. These findings suggest the potential for developing novel anticancer therapies that specifically target EGFR and HER2.