Search Results (1,495)

The space–air–ground integrated network (SAGIN) can provide extensive access, continuous coverage, and reliable transmission for global applications. In scenarios where terrestrial networks are unavailable or compromised, deploying unmanned aerial vehicles (UAVs) within air network offers wireless access to designated regions. Meanwhile, ensuring the connectivity between UAVs as well as between UAVs and ground users (GUs) is critical for enhancing the quality of service (QoS) in SAGIN. In this paper, we consider the 3D deployment problem of multiple UAVs in SAGIN subject to the UAVs’ connection capacity limit and the UAV network’s robustness, maximizing the coverage of UAVs. Firstly, the horizontal positions of the UAVs at a fixed height are initialized using the k-means algorithm. Subsequently, the connections between the UAVs are established based on constraint conditions, and a fairness connection strategy is employed to establish connections between the UAVs and GUs. Following this, an improved genetic algorithm (IGA) with elite selection, adaptive crossover, and mutation capabilities is proposed to update the horizontal positions of the UAVs, thereby updating the connection relationships. Finally, a height optimization algorithm is proposed to adjust the height of each UAV, completing the 3D deployment of multiple UAVs. Extensive simulations indicate that the proposed algorithm achieves faster deployment and higher coverage under both random and clustered distribution scenarios of GUs, while also enhancing the robustness and load balance of the UAV network. Full article

Background/Objectives: The Middle East Respiratory Syndrome Coronavirus (MERS-CoV) is a highly pathogenic virus causing severe respiratory illness, with limited treatment options that are mostly supportive. The success of mRNA technology in COVID-19 vaccines has opened avenues for antibody development against MERS-CoV. mRNA-based antibodies, expressed in vivo, offer rapid adaptability to viral mutations while minimizing long-term side effects. This study aimed to develop a lung-targeted lipid nanoparticle (LNP) system for mRNA-encoding neutralizing nanobodies against MERS-CoV, proposing a novel therapeutic strategy. Methods: An mRNA-encoding nanobody NbMS10 (mRNA-NbMS10) was engineered for enhanced stability and reduced immunogenicity. This mRNA was encapsulated in lung-selective LNPs using microfluidics to form the LNP-mRNA-NbMS10 system. Efficacy was assessed through in vitro assays and in vivo mouse studies, focusing on antigen-binding, neutralization, and sustained nanobody expression in lung tissues. Results: The LNP-mRNA-NbMS10 system expressed the nanobody in vitro, showing strong antigen-binding and significant MERS-CoV pseudovirus neutralization. In vivo studies confirmed selective lung mRNA delivery, with high nanobody expression sustained for up to 24 h, confirming lung specificity and prolonged antiviral activity. Conclusions: Extensive in vitro and in vivo evaluations demonstrate the LNP-mRNA-NbMS10 system’s potential as a scalable, cost-effective, and adaptable alternative to current MERS-CoV therapies. This innovative platform offers a promising solution for preventing and treating respiratory infections, and countering emerging viral threats. Full article

DENV belongs to the Flaviviridae family and possesses a single-stranded RNA genome of positive polarity. DENV infection manifests in mild subclinical forms or severe forms that may be dengue hemorrhagic fever (DHF) or dengue shock syndrome (DSS). Despite a lot of effort worldwide, the exact mechanism underlying the pathogenesis of severe DENV infection remains elusive. It is believed that both host and viral factors contribute to the outcome of dengue disease. The host factors are age at the time of infection, sex, nutrition, and immune status, including the presence of pre-existing antibodies or reactive T cells. Viral factors include the serotype, genotype, and mutation(s) due to error-prone RNA-dependent polymerase leading to the development of quasispecies. Accumulating bodies of literature have depicted that DENV has many ways to invade and escape the immune system of the host. These invading strategies are directed to overcome innate and adaptive immune responses. Like other viruses, once the infection is established, the host also mounts a series of antiviral responses to combat and eliminate the virus replication. Nevertheless, DENV has evolved a variety of mechanisms to evade the immune system. In this review, we have emphasized the strategies that DENV employs to hijack the host innate (interferon, IFN; toll-like receptors, TLR; major histocompatibility complex, MHC; autophagy; complement; apoptosis; RNAi) and adaptive (antibody-dependent enhancement, ADE; T cell immunity) immune responses, which contribute to the severity of DENV disease. Full article

For the first time, we describe phylogenomic signatures of an epidemic lineage of vesicular stomatitis Indiana virus (VSIV). We applied multiple evolutionary analyses to a dataset of 87 full-length genome sequences representing the circulation of an epidemic VSIV lineage in the US between 2019 and 2020. Based on phylogenetic analyses, we predicted the ancestral relationship of this lineage with a specific group of isolates circulating in the endemic zone of Chiapas, Mexico. Subsequently, our findings indicate that the lineage diversified into at least four different subpopulations during its circulation in the US. We identified single nucleotide polymorphisms (SNPs) that differentiate viral subpopulations and assessed their potential relevance using comparative phylogenetic methods, highlighting the preponderance of synonymous mutations during the differentiation of these populations. Purifying selection was the main evolutionary force favoring the conservation of this epidemic phenotype, with P and G genes as the main drivers of the evolution of this lineage. Our analyses identified multiple codon sites under positive selection and the association of these sites with specific functional domains at P, M, G, and L proteins. Based on ancestral reconstruction analyses, we showed the potential relevance of some of the sites identified under positive selection to the adaptation of the epidemic lineage at the population level. Finally, using a representative group of viruses from Colorado, we established a positive correlation between genetic and geographical distances, suggesting that positive selection on specific codon positions might have favored the adaptation of different subpopulations to circulation in specific geographical settings. Collectively, our study reveals the complex dynamics that accompany the evolution of an epidemic lineage of VSIV in nature. Our analytical framework provides a model for conducting future evolutionary analyses. The ultimate goal is to support the implementation of an early warning system for vesicular stomatitis virus in the US, enabling early detection of epidemic precursors from Mexico. Full article

Cancer drug resistance represents one of the most significant challenges in oncology and manifests through multiple interconnected molecular and cellular mechanisms. Objective: To provide a comprehensive analysis of multilevel processes driving treatment resistance by integrating recent advances in understanding genetic, epigenetic, and microenvironmental factors. This is a systematic review of the recent literature focusing on the mechanisms of cancer drug resistance, including genomic studies, clinical trials, and experimental research. Key findings include the following: (1) Up to 63% of somatic mutations can be heterogeneous within individual tumors, contributing to resistance development; (2) cancer stem cells demonstrate enhanced DNA repair capacity and altered metabolic profiles; (3) the tumor microenvironment, including cancer-associated fibroblasts and immune cell populations, plays a crucial role in promoting resistance; and (4) selective pressure from radiotherapy drives the emergence of radioresistant phenotypes through multiple adaptive mechanisms. Understanding the complex interplay between various resistance mechanisms is essential for developing effective treatment strategies. Future therapeutic approaches should focus on combination strategies that target multiple resistance pathways simultaneously, guided by specific biomarkers. Full article

In view of the poor adaptability and uneven coverage of static underwater wireless sensor networks (UWSNs) to environmental changes and the need for dynamic monitoring, a three-dimensional coverage method involving a dynamic UWSNs for marine ranching, based on an improved sparrow search algorithm (ISSA), is proposed. Firstly, the reverse learning strategy was introduced to generate the reverse sparrow individuals and fuse with the initial population, and the individual sparrows with high fitness were selected to improve the search range. Secondly, Levy flight was introduced to optimize the location update of the producer, which effectively expanded the local search capability of the algorithm. Finally, the Cauchy mutation perturbation mechanism was introduced into the scrounger location to update the optimal solution, which enhanced the ability of the algorithm to obtain the global optimal solution. When deploying UWSNs nodes, an autonomous underwater vehicle (AUV) was used as a mobile node to assist the deployment. In the case of underwater obstacles, the coverage hole in the UWSNs was covered by an AUV at specific times. The experimental results show that compared with other algorithms, the ISSA has a shorter mobile path and achieves a higher coverage rate, with lower node energy consumption. Full article

Heat pump-based renewable energy and waste heat recycling have become a mainstay of sustainable heating. Still, configuring an effective control system for these purposes remains a worthwhile research topic. In this study, a Smith-predictor-based fractional-order PID cascade control system was fitted into an actual clean heating renovation project and an advanced fireworks algorithm was used to tune the structural parameters of the controllers adaptively. Specifically, three improvements in the fireworks algorithm, including the Cauchy mutation strategy, the adaptive explosion radius, and the elite random selection strategy, contributed to the effectiveness of the tuning process. Simulation and field investigation results demonstrated that the fitted control system counters the adverse effects of time lag, reduces overshoot, and shortens the settling time. Further, benefiting from a delicate balance between heating demand and supply, the heating system with upgraded management increases the average exergetic efficiency by 11.4% and decreases the complaint rate by 76.5%. It is worth noting that the advanced fireworks algorithm mitigates the adverse effect of capacity lag and simultaneously accelerates the optimizing and converging processes, exhibiting its comprehensive competitiveness among this study’s three intelligent optimization algorithms. Meanwhile, the forecast and regulation of the return water temperature of the heating system are independent of each other. In the future, an investigation into the implications of such independence on the control strategy and overall efficiency of the heating system, as well as how an integral predictive control structure might address this limitation, will be worthwhile. Full article

The state of health (SOH) prediction of lithium-ion batteries is a pivotal function within the battery management system (BMS), and achieving accurate SOH predictions is crucial for ensuring system safety and prolonging battery lifespan. To enhance prediction performance, this paper introduces an SOH prediction model based on an improved sparrow algorithm and support vector regression (ISSA-SVR). The model uses nonlinear weight reduction (NWDM) to enhance the search capability of the Sparrow algorithm and combines a mixed mutation strategy to reduce the risk of local optimal traps. Then, by analyzing the characteristics of different voltage ranges, the charging time from 3.8 V to 4.1 V, the discharge time of the battery, and the number of cycles are selected as the feature set of the model. The model’s prediction capabilities are validated across various working environments and training sample sizes, and its performance is benchmarked against other algorithms. Experimental findings indicate that the proposed model not only confines SOH prediction errors to within 1.5% but also demonstrates robust adaptability and widespread applicability. Full article

Trichoderma is a widely studied ascomycete fungal genus, including more than 400 species. However, genetic information on Trichoderma is limited, with most species reporting only DNA barcodes. Mitochondria possess their own distinct DNA that plays a pivotal role in molecular function and evolution. Here, we report 42 novel mitochondrial genomes (mitogenomes) combined with 18 published mitogenomes of Trichoderma. These circular mitogenomes exhibit sizes of 26,276–94,608 bp, typically comprising 15 core protein-coding genes (PCGs), 2 rRNAs, and 16–30 tRNAs; however, the number of endonucleases and hypothetical proteins encoded in the introns of PCGs increases with genome size enlargement. According to the result of phylogenetic analysis of the whole mitogenome, these strains diverged into six distinct evolutionary branches, supported by the phylogeny based on 2830 single-copy nuclear genes. Comparative analysis revealed that dynamic Trichoderma mitogenomes exhibited variations in genome size, gene number, GC content, tRNA copy, and intron across different branches. We identified three mutation hotspots near the regions encoding nad3, cox2, and nad5 that caused major changes in the mitogenomes. Evolutionary analysis revealed that atp9, cob, nad4L, nad5, and rps3 have been influenced by positive selection during evolution. This study provides a valuable resource for exploring the important roles of the genetic and evolutionary dynamics of Trichoderma mitogenome in the adaptive evolution of biocontrol fungi. Full article

Identifying the parameters of a triple-diode electrical circuit structure in PV modules is a critical issue, and it has been regarded as an important research area. Accordingly, in this study, a differential evolution algorithm (DEA) is hybridized with an electromagnetism-like algorithm (EMA) in the mutation stage to enhance the reliability and efficiency of the DEA. A new formula is presented to adapt the control parameters (mutation factor and crossover rate) of the DEA. Seven different experimental data sets are used to improve the performance of the proposed differential evolution with an integrated mutation per iteration algorithm (DEIMA). The results of the proposed PV modeling method are evaluated with other state-of-the-art approaches. According to different statistical criteria, the DEIMA demonstrates superiority in terms of root mean square error and main bias error by at least 5.4% and 10%, respectively, as compared to other methods. Furthermore, the DEIMA has an average execution time of 27.69 s, which is less than that of the other methods. Full article

Infection by retroviruses and the mobilization of transposable elements cause DNA damage that can be catastrophic for a cell. If the cell survives, the mutations generated by retrotransposition may confer a selective advantage, although, more commonly, the effect of new integrants is neutral or detrimental. If retrotransposition occurs in gametes or in the early embryo, it introduces genetic modifications that can be transmitted to the progeny and may become fixed in the germline of that species. PIWI-interacting RNAs (piRNAs) are single-stranded, 21–35 nucleotide RNAs generated by the PIWI clade of Argonaute proteins that maintain the integrity of the animal germline by silencing transposons. The sequence specific manner by which piRNAs and germline-encoded PIWI proteins repress transposons is reminiscent of CRISPR, which retains memory for invading pathogen sequences. piRNAs are processed preferentially from the unspliced transcripts of piRNA clusters. Via complementary base pairing, mature antisense piRNAs guide the PIWI clade of Argonaute proteins to transposon RNAs for degradation. Moreover, these piRNA-loaded PIWI proteins are imported into the nucleus to modulate the co-transcriptional repression of transposons by initiating histone and DNA methylation. How retroviruses that invade germ cells are first recognized as foreign by the piRNA machinery, as well as how endogenous piRNA clusters targeting the sequences of invasive genetic elements are acquired, is not known. Currently, koalas (Phascolarctos cinereus) are going through an epidemic due to the horizontal and vertical transmission of the KoRV-A gammaretrovirus. This provides an unprecedented opportunity to study how an exogenous retrovirus becomes fixed in the genome of its host, and how piRNAs targeting this retrovirus are generated in germ cells of the infected animal. Initial experiments have shown that the unspliced transcript from KoRV-A proviruses in koala testes, but not the spliced KoRV-A transcript, is directly processed into sense-strand piRNAs. The cleavage of unspliced sense-strand transcripts is thought to serve as an initial innate defense until antisense piRNAs are generated and an adaptive KoRV-A-specific genome immune response is established. Further research is expected to determine how the piRNA machinery recognizes a new foreign genetic invader, how it distinguishes between spliced and unspliced transcripts, and how a mature genome immune response is established, with both sense and antisense piRNAs and the methylation of histones and DNA at the provirus promoter. Full article

Free Space Optical (FSO) communication is extensively utilized in the telecommunication industry for both ground and space wireless links, as well as last-mile applications, as a result of its lesser Bit Error Rate (BER), free spectrum, and easy relocation. However, atmospheric turbulence, also known as Wavefront Aberration (WA), is considered a serious issue because it causes higher BER and affects coupling efficiency. In order to address this issue, a Sensor-Less Adaptive Optics (SLAO) system is developed for FSO to enhance performance. In this research, the compensation of WA in SLAO is obtained by proposing the Brownian motion and Directional mutation scheme-based Coati Optimization Algorithm, BDCOA. Here, the BDCOA is developed to search for an optimum control signal value of actuators in Deformable Mirror (DM). The incorporated Brownian motion and directional mutation are used to avoid the local optimum issue and enhance search space efficiency while searching for the control signal. Therefore, the dynamic control signal optimization for DM using BDCOA helps to enhance the coupling efficiency. Thus, the WAs are compensated for and optical signal concentration is enhanced in FSO. The metrics used for analyzing the BDCOA are Root Mean Square (RMS), BER, coupling efficiency, and Strehl Ratio (SR). The existing methods, such as Simulated Annealing (SA) and Stochastic Parallel Gradient Descent (SPGD), Advanced Multi-Feedback SPGD (AMFSPGD), and Oppositional-Breeding Artificial Fish Swarm (OBAFS), are used for evaluating the performance of BDCOA. The RMS of BDCOA for iterations 500 is 0.12, which is less than that of the SA-SPGD and OBAFS. Full article

The transient stability control for disturbances in microgrids based on a lithium-ion battery–supercapacitor hybrid energy storage system (HESS) is a challenging problem, which not only involves needing to maintain stability under a dynamic load and changing external conditions but also involves dealing with the energy exchange between the battery and the supercapacitor, the dynamic change of the charging and discharging process and other factors. This paper focuses on the bus voltage control of HESS under load mutations and system uncertainty disturbances. A BP Neural Network-based Active Disturbance Rejection Controller (BP-ADRC) is proposed within the traditional voltage-current dual-loop control framework, leveraging uncertainty quantification. Firstly, system uncertainties are quantified using system-identification tools based on measurable information. Subsequently, an Extended State Observer (ESO) is designed to estimate the total system disturbance based on the quantified information. Thirdly, an adaptive BP Neural Network-based Active Disturbance Rejection Controller is studied to achieve transient stability control of disturbances. Robust controllers, PID controllers and second-order linear Active Disturbance Rejection Controllers are employed as benchmark strategies to design simulation experiments. Simulation results indicate that, compared to other benchmark strategies, the BP-ADRC controller based on uncertainty quantification exhibits superior tracking and disturbance-rejection performance in transient stability control within microgrids of hybrid energy storage systems. Full article

Numerous varieties of Capsicum annuum L. with multiple valuable traits, such as adaptation to biotic and abiotic stress factors, can be found in south-east Romania, well known for vegetable cultivation and an important area of biodiversity conservation. To obtain useful information about sustainable agriculture, management, and conservation of local pepper varieties, we analyzed the genetic diversity and conducted deep molecular characterization using whole genome resequencing (WGS) for variant/mutation detection. The pepper varieties used in the present study were registered by VRDS in the ISTIS catalog between 1974 and 2019 and maintained in conservative selection; however, no studies have been published yet using WGS analysis in order to characterize this specific germplasm. The genome sequences, annotation, and alignments provided in this study offer essential resources for genomic research as well as for future breeding efforts using the C. annuum local varieties. Full article

Antimicrobial resistance (AMR) in Mycoplasma hyopneumoniae, the causative agent of Enzootic Pneumonia in swine, poses a significant challenge to the swine industry. This review focuses on the genetic foundations of AMR in M. hyopneumoniae, highlighting the complexity of resistance mechanisms, including mutations, horizontal gene transfer, and adaptive evolutionary processes. Techniques such as Whole Genome Sequencing (WGS) and multiple-locus variable number tandem repeats analysis (MLVA) have provided insights into the genetic diversity and resistance mechanisms of M. hyopneumoniae. The study underscores the role of selective pressures from antimicrobial use in driving genomic variations that enhance resistance. Additionally, bioinformatic tools utilizing machine learning algorithms, such as CARD and PATRIC, can predict resistance traits, with PATRIC predicting 7 to 12 AMR genes and CARD predicting 0 to 3 AMR genes in 24 whole genome sequences available on NCBI. The review advocates for a multidisciplinary approach integrating genomic, phenotypic, and bioinformatics data to combat AMR effectively. It also elaborates on the need for refining genotyping methods, enhancing resistance prediction accuracy, and developing standardized antimicrobial susceptibility testing procedures specific to M. hyopneumoniae as a fastidious microorganism. By leveraging contemporary genomic technologies and bioinformatics resources, the scientific community can better manage AMR in M. hyopneumoniae, ultimately safeguarding animal health and agricultural productivity. This comprehensive understanding of AMR mechanisms will be beneficial in the adaptation of more effective treatment and management strategies for Enzootic Pneumonia in swine. Full article