bluuebunny/crossref_metadata_2025_split huggingface dataset:
Spoofify: A Hybrid Anti-Spoofing Facial Recognition System
Authors: Zia Ayesha , Ejaz Syeda Ravia , Ahmad Sabeerah , Syed Farrukh Hasan 
Published: June 2024

Abstract

Facial recognition systems have gained widespread adoption as replacements for conventional authenti- cation methods due to their robustness, offering bet- ter security compared to systems such as PINs, pass- words, and fingerprints. However, despite their re- silience, facial recognition remains vulnerable to var- ious attacks , including picture, video, and replay at- tacks. To mitigate these risks, the implementation of a robust anti-spoofing mechanism is imperative. In this paper, we propose a novel hybrid anti-spoofing facial recognition system that combines unsupervised and supervised approaches to address spoofing vul- nerabilities effectively. Leveraging Generative Adver- sarial Networks (GANs) as the cornerstone model for anti-spoofing, our system employs their efficiency in feature learning to discriminate between genuine and spoofed images. The user identification is performed using a K-Nearest Neighbors (KNN) classifier, which analyzes facial features to match individuals against a database of registered users. We evaluate the perfor- mance of our proposed system on the CelebA-Spoof and CASIA-FASD Datasets, achieving a commend- able accuracy rate of 69% on CASIA-FASD. Through rigorous experimentation and analysis, we demon- strate the efficacy and reliability of our hybrid anti- spoofing facial recognition system, offering a promis- ing solution to enhance security in real-world appli- cations. The source code for the approach will be re- leased on: https://github.com/ayeshazia99/spoofify- a-hybrid-antispoofing-facial-recognition-system
Modeling of AP-HTPB Solid Propellant Viscoelastic Behavior Using Modified Maxwell Model
Authors: Eldin A. F. Nour , Adel Walid. M. , sabbagh Ahmed El 
Published: June 2024

Abstract

In this research, an initial exploration of the viscous effects present in heterogeneous solid rocket propellant is conducted through experimental analysis. To achieve this, uniaxial tensile and relaxation experiments were conducted on the Joint Army-Navy-NASA-Air Force Propulsion Committee (JANNAF) standard[1], specimens using the Zwick Z050 universal testing machine. The work involved conducting destructive tensile tests at different strain rates and relaxation tests at different strain levels and temperatures, with three values being considered. The resulting data from the experiments are illustrated in appropriate diagrams, and depending on these data, the viscoelastic behavior of this material was confirmed. Additionally, mathematical modeling of this studied phenomenon using the generalized Maxwell model, identified on the basis of experimental data, is presented. The material parameters of the constitutive model are determined numerically using MATLAB software based on the Prony series procedure. The efficiency of the model and the identification approach are discussed. A high agreement between the calculation and the experimental results was found based on the Prony coefficients of relaxation modulus, with a maximum error of about 2.35%. Finally, numerical modeling of the relaxation tests was conducted to simulate the stress relaxation behavior of the AP-HTPB composite solid propellant using the ANSYS program. The Prony coefficients were added to the nonlinear viscoelastic components to define the material model in ANSYS, and the maximum difference between the numerical and experimental results is 3.98%.
Signal-amplified surface-enhanced Raman scattering using core/shell satellite nanoparticles for norovirus detection
Authors: Park Enoch Y. , Maehata Syuei , Khoris Indra Memdi , Achadu Ojodomo 
Published: June 2024

Abstract

The development of highly sensitive, rapid, and simple detection techniques for today's aging society is an essential subject, as seen by the rising prevalence of viral infections. Herein, we explore the development of an innovative approach to amplify the signal of a surface-enhanced Raman scattering (SERS)-based detection system using a novel nanotag: Au@Ag NPs covered by satellite AuNPs and conjugated by 4-mercaptbenzoic acid (4-MBA) as a Raman tag (Au@Ag-MBA-AuNPs). The Au@Ag-MBA-AuNPs nanotags showed strong SERS activities with an enhancement factor in the 108 order of magnitude. This indicates the formation of many hot spots due to the combination of core-shell nanoparticles and satellite AuNPs on the surface of Au@Ag-MBA NPs. The newly fabricated nanotags were employed in a small-sized Palmtop Raman spectrometer. A concentration-dependent increase in SERS intensity was observed in the norovirus-like particle (NoV-LP) concentration range of 10 fg/mL to 100 pg/mL with a detection limit of 0.76 fg/mL. Even in the severe interfering matrices, this detection method's coefficient of variation was less than 10%. This detection system was approximately 107 times more sensitive than commercially available ELISA kits. Norovirus in clinical samples was detected over a wide concentration range of 1.0 × 101 – 1.0 × 106 RNA copy number/mL with a detection limit of 7.8 RNA copy number/mL, indicating sensitivity comparable to real-time PCR. These results suggest that this detection system is stable in a complex matrix and has the potential for detecting clinical samples with a small Palmtop Raman spectrometer.
Multi-omics investigation reveals unique markers underpinning the success of Klebsiella pneumoniae over closely related species
Authors: Swiatek Lena-Sophie , Surmann Kristin , Eger Elias , Müller Justus Ursus , Salazar Manuela Gesell , Heiden Stefan E. , Werner Guido , Hübner Nils-Olaf , Bohnert Jürgen A. , Becker Karsten , Völker Uwe , Schwabe Michael , Schaufler Katharina 
Published: June 2024

Abstract

Background
The Klebsiella pneumoniae (KP) species complex comprises KP as the predominant species, and four others including Klebsiella variicola (KV) and Klebsiella quasipneumoniae (KQ), all capable of causing clinical infections and often challenging to differentiate. The emergence of multidrug-resistant and hypervirulent KP strains has led to severe infections with limited treatment options, underscoring the need to understand the factors contributing to KP's prevalence.
Methods
This study compared globally disseminated KP lineages with less abundant KV and KQ strains across multiple omics levels to identify characteristics potentially explaining KP's success over closely related species.
Results
We identified 107 genes of the core genome in eight clinical KP strains from four different sequence types (STs). Transcriptome and proteome analyses in human urine-like medium revealed different regulatory patterns between KP and KV strains, with metabolic responses playing a pivotal role. A total of 193 genes specific to the investigated KP STs were identified, exhibiting differential expression at the transcriptomic and/or proteomic levels. Comparison of these regulated genes to over 6,000 publicly available Klebsiella genomes highlighted genes adaptively regulated or uniquely present in KP genomes. For example, a gene cluster for the cellobiose phosphotransferase system, previously linked to bacterial virulence and biofilm formation, was found exclusively in KP.
Conclusion
Our study underscores the metabolic flexibility of KP strains in response to specific environmental conditions, potentially crucial for their success as opportunistic pathogens. We identified markers enriched in KP STs, providing a foundation for future investigations and potential utilization in alternative treatment strategies to conventional antibiotics.
Enhancing Speech Privacy through Dynamic Chaos- Driven Scrambling with Nested Segmentation
Authors: MERRAD Ahmed , NOURI Abdelkader Nabil 
Published: June 2024

Abstract

Designing speech cryptosystems presents huge challenges. This paper introduces a novel speech scrambling approach that leverages the strength of two chaotic maps and nested segments. Initially, the process of the scrambling determines the starting point of each segment after diverse parameters are provided, such as the starting point of the first piece and the length of each segment. Then, based on a rearranged chaotic signal generated by the Tent map, the process commences permuting the samples of the nested segments with a specific order that determined according to another rearranged chaotic signal created using the Logistic map. In order to allow the possibility of permuting the samples beyond their initial segment, the process of scrambling iterates throughout the whole signal several times, using a supplied key. The achieved outcomes shows the efficiency of the planned method, as SNR values can reach − 3 and infinity in scrambling and unscrambling operations, respectively, as well, the waveforms vividly depict the flawless diffusion of the samples across the length of the signal.
Uncovering Naringin’s Anticancer Mechanisms in Glioblastoma via Molecular Docking and Network Pharmacology Approaches
Authors: Rajendran Arunraj Tharamelveliyil , Rajesh Gupta Dheeraj , Ashtekar Harsha , Sairam Anusha , Kumar Pankaj , Vadakkepushpakath Anoop Narayanan 
Published: June 2024

Abstract

Naringin, a flavonoid, exhibits diverse therapeutic properties and has been proven to exert cytotoxic effects on cancer cells. Nevertheless, the precise mechanism of naringin maintaining its cytotoxic effect on glioblastoma (GBM) remains unknown. Thus, the current study aimed to establish a plausible cellular mechanism for Naringin's inhibition of GBM. We employed various system biology techniques to forecast the primary targets, including gene ontology and cluster analysis, KEGG enrichment pathway estimation, molecular docking, MD (molecular dynamic) simulation and MMPBSA analysis. Glioblastoma target sequences were obtained via DisGeNet and Therapeutic Target Prediction, aligned with naringin targets, and analyzed for gene enrichment and ontology. Gene enrichment analysis identified the top ten hub genes. Further, molecular docking was conducted on all identified targets. For molecular dynamics modelling, we selected the two complexes that exhibited the most docking affinity and the two most prominent genes of the hub identified through analysis of the enrichment of genes. The PARP1 and ALB1 signalling pathways were found to be the main regulated routes. Naringin exhibited the highest binding potential of -12.90 kcal/mol with PARP1 (4ZZZ), followed by ABL1 (2ABL), with naringin showing a -8.4 kcal/mol binding score, as determined by molecular docking. The molecular dynamic approach and MM-PBSA investigation revealed that the complex of Naringin, with 4ZZZ (PARP1) and, 2ABL(ABL1), demonstrated the highest stability. Analyses of the signalling pathway suggested that naringin may have anticancer effects against GBM by influencing the protein PARP and ALB1 levels. Since it was developed In-silico, this study will expand to validate the mechanism in wet labs.
A High Selective and Q Factor 4-Channel All Optical Demultiplexer: Numerical and Analytical Evaluation
Authors: Vali‑Nasab Aziz-Morad , Hasanvand Javad , Mir Ali , Hamouleh-Alipour Abbas 
Published: June 2024

Abstract

Photonic crystals (PCs) are a field that is very important and interested in the design of a wide range of devices to control electromagnetic waves in different frequency regimes. Here, We try to introduce an all-optical device with practical applications such as fast response and high accuracy that have very critical and benchmark development in optical devices for photonic integrated circuits. In this paper, a novel modified shape 4-channel demultiplexer based on 2D-PCs by using suitable ring resonators is proposed to control light. The presented configuration includes three parts such as a direct input waveguide, a ring resonator, and four indirect waveguides for outputs of optical waves. Numerical analysis is investigated based on finite difference time domain (FDTD) method to achieve a four-band demultiplexer that has the capability to control the electromagnetic waves. Numerical results show that not only average quality factor (Q) of the 2D-PCs demultiplexer is 3969.25, but also average cross-talking value is -30.38 dB, the average transmission coefficient (T) is 0.998 and the lowest and highest channel bandwidths are 0.3 and 0.6 nm, respectively. Also, the minimum and maximum distance between any two adjacent channels is 0.6 and 1.1 nm. The all optical photonic demultiplexer has a compact footprint and occupies an area of 302 µm² and its operation is in long wavelength region. Based on the abovementioned practical performance and results, the presented optical demultiplexer is highly accurate by using a robust and low power-area that can be nominated to use photonic integrated circuits (PIC).
Towards Text Contextual Understanding: Text Feature Fusion GAN for Text-to-Image Generation
Authors: Jiang Xiaoyan , Chen Jize , Zhang Juan , Chen Zhichao , Gao Yongbin , Wu Xiang 
Published: June 2024

Abstract

Understanding texts and generating high-resolution realistic images from text descriptions is a meaningful and challenging topic. Most existing text-to-image generation methods follow the multi-stage generative adversarial network (GAN) framework. Therefore, the image quality of current stages relies heavily on the images generated in the previous stages. Additionally, current text-to-image generators do not so well guarantee a consistent relationship between the text description and the generated image. To address the above issues, we present a novel Text Feature Fusion GAN (TF-GAN) architecture, emphasizing on selecting useful local words and extracting semantic consistent global sentence features. First, useful keywords are extracted in the text description by the proposed sentence fusion attention mechanism (SFAttn) to optimize image features in early stages and provide fine-grained details for the images of the later stage. Second, we propose a new Conditional Fusion Block (CFBlock), which combines multiple non-linear affine transformations to constrain images on a global semantic level. This allows the model to more deeply fuse sentence and image features, leading to more semantically consistent generated images. Extensive experiments and Comparison with other state-of-the-art methods on the Caltech-UCSD Birds 200 (CUB) dataset and the Microsoft Common Objects in Context (COCO) dataset show that our generated images are more photo-realistic and closer to the text descriptions.
The Potential Chemotherapeutic Effect of Coenzyme Q10 against Liver Injury in a Leukemia Rat Model by 7,12-dimethylbenzanthracene; histological and morphometric study
Authors: Alshali Rasha A. 
Published: June 2024

Abstract

Introduction:
Coenzyme Q10 (CoQ10) is an essential cofactor in the mitochondrial electron transport chain that benefits liver disorders. The polycyclic aromatic hydrocarbon (PAH) 7,12-dimethylbenz[a]-anthracene (DMBA) originates and promotes carcinogenesis. This study investigated the potential anti-leukemic effects of CoQ10 in a DMBA-induced leukemic rat model. The biochemical, histological, and immunohistochemical analysis of the liver was assessed to determine that CoQ10 would modify liver injury caused by DMBA-induced Leukemia in rat model.
Methods
Forty adult male albino rats were split equally into four groups: control group (Group I), coenzyme Q10 group (Group II), DMBA group (Group III), and coenzyme Q10 plus DMBA group (Group IV). The first DMBA dose of 40 mg/kg body weight produced leukaemia in all rats except GI and GII rats. Three further injections were given at a rate of 30 mg/kg body weight every two weeks for a total of 6 weeks. Body weight and blood samples were evaluated once the experiment was complete. Biochemical, histological, immunohistochemical, and morphometric analyses were performed on liver tissue.
Results
CoQ10 treatment significantly reduced liver enzyme levels relative to leukemia-model animals. Malondialdehyde (MDA) levels were much lower in CoQ10 group compared to DMBA group, whereas glutathione (GSH) and Superoxide dismutase (SOD) levels were significantly increased. Histopathological examination of rat DMBA livers revealed a loss of normal hepatic architecture, which was restored after administration of CoQ10.
Conclusion
Treatment with CoQ10 attenuated the alleviation of DMBA-induced leukemia in rats, claiming its potential as an effective therapy for leukaemia and liver impairment caused by leukaemia.
Curcumin ameliorates hepatic steatosis and insulin resistance through the JNK2/FOXO1/Bcl6 axis and regulate the intestinal flora structure
Authors: Yang Ju , Song Biao zhang , Zhang Feng , Liu Bing , Yan Ji ai , Wang Yingyu , Sun Jing , Zhao Cheng guang , Li Dan , Cao Hong 
Published: June 2024

Abstract

Curcumin, a polyphenol extracted from the plant turmeric rhizoma, is well known for its strong antioxidant capacity and beneficial effects on the treatment of obesity induced by a high-fat diet in mice. However, the exact mechanism of action by which it improves obesity remains elusive. We hypothesize that curcumin exerts its anti-obesity effects on high-fat-diet (HFD) induced obesity through both direct effects on metabolic pathways and indirect effects on metabolism by improving the intestinal flora structure. The aim of this study was to investigate the effect of curcumin on the biological phenotype of HFD-induced obese mice, to determine the related metabolic pathways and to determine whether the intestinal flora is involved. C57BL/6 mice were fed a HFD for 8 weeks and then gavaged with 200 mg/kg curcumin or the same volume of vehicle for 16 weeks. The body weight, blood glucose level, blood lipid level, insulin resistance and oxidative stress level of the mice were detected to determine the effect of the treatment on lipid metabolism. Liver transcriptome analysis combined with qPCR and cell experiments revealed that curcumin improves hepatic steatosis and insulin resistance in mice fed a high-fat diet by downregulating the JNK2/FOXO1/Bcl6 axis. Curcumin treatment can regulate the composition and structure of gut microbiota in high-fat diet-fed mice, increase gut microbiota diversity, and increase the relative abundance of beneficial bacteria such as Coriobacteriaceae, Mailhella, Faecalibaculum, Phocaeicola vulgatus, Parvibacter vulgatus, and Bacteroides intestinalis, which are associated with obesity and metabolic disorders, while reducing the relative abundance of harmful bacteria such as Alistipes, Oscillibacter, Lactobacillus johnsonii, and Acutalibacter muris. In conclusion, curcumin ameliorated hepatic steatosis and insulin resistance in HFD-fed mice by down-regulating hepatic JNK2/FOXO1/Bcl6 axis and altering the composition and structure of gut microbiota.
Analysis and control of new invariant JN.1 under generalized incidence rate for low immune: Mathematical modeling and bifurcation
Authors: Junjua Moin-ud-Din , Faheem Muhammad , Ahmad Aqeel , Akram Muhammad Saeed , Hincal Evren 
Published: June 2024

Abstract

The goal of this study is to analyze and control the new variant JN.1 under generalized incidence rate for low immune individuals by taking combine measures of vaccination as well as quarantine. By incorporating vaccination and quarantine together with generalized incidence rate a new mathematical model is developed based on generated novel hypothesis. To ascertain the stable status of the recently constructed SEVIQR system which is analysis both qualitatively and quantitatively to understand the dynamics of spread as well as control under bounded domain including bifurcation analysis. Stability analysis have been derived on local as well as global scale using Lyapunov’s first derivative functions to evaluate the overall effects of disease propagation and control. The existence and positive solutions property is also derived under non local operator. To ensure reliable bounded findings, the boundedness and uniqueness are examined, those are necessary characteristics for epidemic models. We use Lipschit’z conditions and linear growth to fully validate the global derivative that characterizes the rate of change of disease impact on each sub-compartment. The bounded approximate solutions for new invariant JN.1 derived sing Mittag-Leffler kernel with fractal fractional operator for continuous monitoring. Simulations are derived using MATLAB coding to observe the real impact of spread as well as control of virus caused by new variant JN.1 with generalized incidence rate, a variety of factors are imposed with constant monitoring. To observe the asymptomatic and symptomatic consequences of the corona virus illness under new variant JN.1, simulations are developed with combine actions of vaccination as well as quarantine effects for low immunity people to capture the disease’s true behavior. To comprehend how the virus spreads for low immune individuals and to create efficient control plans based on the results that make sense such investigations are valuable.
CA1 Engram Cell Dynamics Before and After Learning
Authors: Ramirez Steve , Monasterio Amy , Lienkaemper Caitlin , Coello Siria , Ocker Gabriel , Scott Benjamin 
Published: June 2024

Abstract

A fundamental question in neuroscience is how memory formation shapes brain activity at the level of populations of neurons. Recent studies of hippocampal ‘engram’ cells, identified by immediate-early genes (IEGs) induced by learning, propose that these populations act as a neuronal substrate for memory storage. The current framework for engram formation proposes that cells join ensembles based on increased intrinsic excitability, and that after initial learning, they co-activate to support memory retrieval. However, direct evidence of how engram population dynamics evolve across learning is limited. Here we combined activity-dependent genetic tagging and two-photon calcium imaging to characterize CA1 engram population activity before and after learning. We observed that spontaneous activity two days before learning predicted genetic tagging, consistent with a model in which spontaneous fluctuations bias cells into forming engram assemblies. Surprisingly, we were unable to detect increased spontaneous activity rates or pairwise correlations amongst tagged CA1 neurons after learning. These results were consistent with computational network models that incorporate strong and specific inhibitory connections, supporting the idea that excitatory/inhibitory balance in CA1 may play a key role in engram dynamics. Together these results highlight a potential role for slow time scale excitability fluctuations in driving engram formation and suggest that excitatory-inhibitory balance may regulate engram cell co-activation.