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Fear effect on the mobility of individuals in a spatially heterogeneous environment: a delayed diffusive SPIR epidemic model
Abstract As the fear of infection is a crucial factor in the progress of the disease in the population. We aim, in this study, to investigate a susceptible-protected-infected-recovered (SPIR) epidemic model with mixed diffusion modeled by local and nonlocal diffusions. These types of diffusion are used to model the fear effect of being infected by the population. The model is shown to be well-posed; the solution exists, is positive, and is unique. The variational expression is obtained to determine threshold role of $$\mathfrak {R}_0$$ , also known as the basic reproduction number. Indeed, for $$\mathfrak {R}_0<1$$ , we show that the epidemic will extinct, corresponding to the global asymptotic stability of the infection-free equilibrium state. However, when $$\mathfrak {R}_0>1$$ , the existence of the infection equilibrium state and the uniform persistence of the solution have been proved. The Lyapunov function have been used to show the global asymptotic stability of the infection equilibrium state. Moreover, we compared the obtained results with the classical SIR epidemic model for determining the required protection function for stopping the disease, which can be obtained by reducing $$\mathfrak {R}_0$$ below one.
Induction and characterization of oral submucous fibrosis model with different pathological stages in rats and mice
Predicting bone cancer drugs properties through topological indices and machine learning
Abstract Chemical graph theory and topological indices are key tools in the study of molecular structures and their properties. This research explores anticancer drugs using neighborhood degree-based topological indices and compares their efficacy through regression and machine learning models. The QSPR approach is applied to 15 anticancer drugs by constructing neighborhood-based molecular graphs, and calculating their respective topological indices. Regression models like quadratic, cubic, and random forest are employed to predict response metrics including like boiling point, refractivity, and surface area of the drugs. Comparative studies indicate that quadratic models provide better predictive performance then their cubic counterparts in most scenarios. Random forest models also demonstrate satisfactory accuracy with smaller error bounds. The present findings highlight the usefulness of topological indices in chemoinformatics and their application in predicting drug response.
A novel numerical investigation of fiber Bragg gratings with dispersive reflectivity having polynomial law of nonlinearity
Polydopamine-coated double emulsion capsules for on-demand drug release with reduced passive leakage
Multi-fault diagnosis and damage assessment of rolling bearings based on IDBO-VMD and CNN-BiLSTM
Analyzing blockchain adoption in management operations through an extended UTAUT model in the Libyan context
Potential application of Healitide-GP1, a novel antibacterial peptide, in wound healing: in vitro studies
Gallic acid alleviates hippocampus and cerebellum injuries in a rat model of hepatic encephalopathy
Machine learning to predict bacteriuria in the emergency department
Influence of ceramic thermal barrier coating on diesel engine performance using scum oil biodiesel at different compression ratios
Hyperspectral imaging and K-means clustering for material structure classification and detection of unmanned aerial vehicles
Abstract Unmanned aerial vehicles (UAVs) have become increasingly widespread in a variety of industries due to their versatility and efficiency in applications such as agriculture, surveillance, logistics, and construction. However, their rapid adoption has introduced challenges related to detection and classification, especially in the context of privacy, public safety, and national security. Conventional UAV detection methods, such as radar, thermal imaging, and acoustic systems, face limitations in accurately distinguishing between UAVs and other airborne objects. Additionally, these systems often fail to differentiate between UAVs constructed from different materials, such as carbon fiber-reinforced polymers (CFRP) and glass fiber-reinforced polymers (GFRP), which significantly affect the UAV’s radar and thermal profiles. This paper presents a promising approach for UAV detection based on the material composition of their structures using hyperspectral imaging (HSI) and K-Means (K-M) clustering. Using the proposed approach, we found that CFRP can be detected at 700 nm. While GFRP can be detected at 530 nm. By applying the K-M clustering algorithm to the spectral data, we successfully classify these materials without prior knowledge of object types. The proposed method shows high effectiveness in accurately distinguishing between UAVs based on their material composition, offering improvements over traditional detection methods that rely on shape, size, or heat signatures. This research contributes a new dimension to UAV detection by focusing on material-specific classification, providing significant potential for applications in security and surveillance, where understanding the structural composition of a UAV is critical for effective identification and mitigation strategies.
The cAMP-PKA signaling initiates mitosis by phosphorylating Bora
4D structural biology–quantitative dynamics in the eukaryotic RNA exosome complex
Abstract Molecular machines play pivotal roles in all biological processes. Most structural methods, however, are unable to directly probe molecular motions. Here, we demonstrate that dedicated NMR experiments can provide quantitative insights into functionally important dynamic regions in very large asymmetric protein complexes. We establish this for the 410 kDa eukaryotic RNA exosome complex that contains ten distinct protein chains. Methyl-group and fluorine NMR experiments reveal site-specific interactions among subunits and with an RNA substrate. Furthermore, we extract quantitative insights into conformational changes within the complex in response to substrate and subunit binding for regions that are invisible in static cryo-EM and crystal structures. In particular, we identify a flexible plug region that can block an aberrant route for RNA towards the active site. Based on molecular dynamics simulations and NMR data, we provide a model that shows how the flexible plug is structured in the open and closed conformations. Our work thus demonstrates that a combination of state-of-the-art structural biology methods can provide quantitative insights into large molecular machines that go significantly beyond the well-resolved and static images of biomolecular complexes, thereby adding the time domain to structural biology.
The genetics of extrinsic postzygotic selection in a migratory divide between subspecies of the Swainson’s thrush
Abstract Extrinsic postzygotic isolation, where hybrids experience reductions in fitness due to a mismatch with their environment, is central to speciation. Knowledge of genetic variants that underlie extrinsic isolation is crucial for understanding the early stages of speciation. Differences in seasonal migration are strong candidates for extrinsic isolation (e.g., if hybrids take intermediate and inferior routes compared to pure forms). Here, we used a hybrid zone between two subspecies of the songbird Swainson’s thrush (Catharus ustulatus) with different migratory routes and tests for viability selection (locus-specific changes in interspecific heterozygosity and ancestry mismatch across age classes) to gain insight into the genetic basis of extrinsic isolation. Using data from over 900 individuals we find strong evidence for viability selection on both interspecific heterozygosity and ancestry mismatch at loci linked to migration. Much of this selection was dependent on genome-wide ancestry; as expected, a subset of hybrids exhibited reduced viability, but remarkably, another subset appears to fill an unoccupied fitness peak within the species, exhibiting higher viability than even parental forms. Many of the variants that influence hybrid viability appear to occur in structural variants, including a putative pericentric inversion. Our study emphasizes the importance of epistatic interactions and structural variants in speciation.
An approach using geometric diagrams to generic Bell inequalities with multiple observables
Exploring biogeochemical transformation in a hypertrophic lake with dual focus on environmental cues and potential functional fingerprints of littoral bacteria
Impact of Micro-RNAs as biomarkers for end-stage renal disease related to hypertension and diabetes
Abstract End-stage renal disease (ESRD) is a rapidly increasing global health and healthcare challenge. MicroRNAs (miRNAs) have been implicated in kidney disease due to their role in apoptosis, cell proliferation, differentiation, and development. The aim of this study was to determine the role of miRNA-21-5p, miRNA-126-3p, and miRNA-192-5p in the prognosis of ESRD. In addition, we aimed to evaluate the discriminatory ability of these miRNAs as biomarkers for ESRD in relation to the comorbidities of hypertension (HTN) and diabetes mellitus (DM). One hundred and ten individuals were recruited for our study and divided into three groups: group 1 included 40 ESRD patients with hypertension, group 2 included 40 ESRD patients with diabetes, and group 3 (n = 30) served as healthy controls. Real-time polymerase chain reaction (RT-PCR) was used to quantify the above miRNAs. Patients with ESRD were found to have lower levels of miRNA-126-3p and higher levels of miRNAs-21-5p and − 192-5p. Furthermore, the accuracies of ROC analyses for miR-21-5p, miR-126-3p, and miR-192-5p were 96.65%, 99.5%, and 93.35% in ESRD with HTN and 95%, 71.5%, and 93% in ESRD with DM. Dysregulation of these miRNAs is associated with the development of ESRD and could be used as biomarkers for ESRD. This study briefly outlines the challenges associated with miRNA research and the potential use of miRNA molecules in the management of ESRD, proposing a research approach emphasizing the development of standardized and reliable biomarkers for therapeutic use. Despite the promising diagnostic utility demonstrated, the lack of cross-validation and external validation remains an important limitation. Future large-scale, independent studies are essential to confirm these findings and ensure broader applicability.