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Bats, with their virus tolerance, social behaviors, and mobility, are reservoirs for emerging viruses, including coronaviruses (CoVs) known for genetic flexibility. Studying the cophylogenetic link between bats and CoVs provides vital insights into transmission dynamics and host adaptation. Prior research has yielded valuable insights into phenomena such as host switching, cospeciation, and other dynamics concerning the interaction between CoVs and bats. Nonetheless, a distinct gap exists in the current literature concerning a comparative cophylogenetic analysis focused on elucidating the contributions of sequence fragments to the co-evolution between hosts and viruses. In this study, we analyzed the cophylogenetic patterns of 69 host–virus connections. Among the 69 host–virus links examined, 47 showed significant cophylogeny based on ParaFit and PACo analyses, affirming strong associations. Focusing on two proteins, ORF1ab and spike, we conducted a comparative analysis of host and CoV phylogenies. For ORF1ab, the specific window ranged in multiple sequence alignment (positions 520–680, 770–870, 2930–3070, and 4910–5080) exhibited the lowest Robinson–Foulds (RF) distance (i.e., 84.62%), emphasizing its higher contribution in the cophylogenetic association. Similarly, within the spike region, distinct window ranges (positions 0–140, 60–180, 100–410, 360–550, and 630–730) displayed the lowest RF distance at 88.46%. Our analysis identified six recombination regions within ORF1ab (positions 360–1390, 550–1610, 680–1680, 700–1710, 2060–3090, and 2130–3250), and four within the spike protein (positions 10–510, 50–560, 170–710, and 230–730). The convergence of minimal RF distance regions with combination regions robustly affirms the pivotal role of recombination in viral adaptation to host selection pressures. Furthermore, horizontal gene transfer reveals prominent instances of partial gene transfer events, occurring not only among variants within the same host species but also crossing host species boundaries. This suggests a more intricate pattern of genetic exchange. By employing a multifaceted approach, our comprehensive strategy offers a nuanced understanding of the intricate interactions that govern the co-evolutionary dynamics between bat hosts and CoVs. This deeper insight enhances our comprehension of viral evolution and adaptation mechanisms, shedding light on the broader dynamics that propel viral diversity.

In this paper, the prediction of over-the-top service quality is discussed, which is a promising way for mobile network engineers to tackle service deterioration as early as possible. Currently, traditional mobile network operation often takes appropriate remedial measures, when receiving customers’ complaints about service problems. With the popularity of over-the-top services, this problem has become increasingly serious. Based on the service perception data crowd-sensed from massive smartphones in the mobile network, we first investigated the application of multi-label ReliefF, a well-known method of feature selection, in determining the feature weights of the perception data and propose a unified multi-label ReliefF (UML-ReliefF) algorithm. Then a feature-weighted multi-label k-nearest neighbor (ML-kNN) algorithm is proposed for the key quality indicators (KQI) prediction, by combining the UML-ReliefF and ML-kNN together in the learning. The experimental results for web browsing service show that UML-ReliefF can effectively identify the most influential features of the data and thus, lead to better performance for KQI prediction. The experiments also show that the feature-weighted KQI prediction is superior to its unweighted counterpart, since the former takes full advantage of all the features in the learning. Although there is still much room of improvement in the precision of the prediction, the proposed method is highly potential for network engineers to find the deterioration of service quality promptly and take measures before it is too late.

We numerically and experimentally demonstrate a terahertz metadevice consisting of split-ring resonators (SRRs) present within square metallic rings. This device can function as a dual-band polarization converter by breaking the symmetry of SRRs. Under x-polarized incidence, the metastructure is able to convert linearly polarized (LP) light into a left-hand circular-polarized (LCP) wave. Intriguingly, under y-polarized incidence, frequency-dependent conversion from LP to LCP and right-hand circular-polarized (RCP) states can be achieved at different frequencies. Furthermore, reconfigurable LCP-to-LP and RCP-to-LP switching can be simulated by integrating the device with patterned graphene and changing its Fermi energy. This dual-band and multi-state polarization control provides an alternative solution to developing compact and multifunctional components in the terahertz regime.

DNA methyltransferases (DNMTs) including DNMT1 are a conserved family of cytosine methylases that play crucial roles in epigenetic regulation. The versatile functions of DNMT1 rely on allosteric networks between its different interacting partners, emerging as novel therapeutic targets. In this work, based on the modeling structures of DNMT1-ubiquitylated H3 (H3Ub)/ubiquitin specific peptidase 7 (USP7) complexes, we have used a combination of elastic network models, molecular dynamics simulations, structural residue perturbation, network modeling, and pocket pathway analysis to examine their molecular mechanisms of allosteric regulation. The comparative intrinsic and conformational dynamics analysis of three DNMT1 systems has highlighted the pivotal role of the RFTS domain as the dynamics hub in both intra- and inter-molecular interactions. The site perturbation and network modeling approaches have revealed the different and more complex allosteric interaction landscape in both DNMT1 complexes, involving the events caused by mutational hotspots and post-translation modification sites through protein-protein interactions (PPIs). Furthermore, communication pathway analysis and pocket detection have provided new mechanistic insights into molecular mechanisms underlying quaternary structures of DNMT1 complexes, suggesting potential targeting pockets for PPI-based allosteric drug design.