A cross-species framework for classifying sound-movement couplings.

Sound and movement are entangled in animal communication. This is obviously true in the case of sound-constituting vibratory movements of biological structures which generate acoustic waves. A little less obvious is that other moving structures produce the energy required to sustain these vibrations. In many species, the respiratory system moves to generate the expiratory flow which powers the sound-constituting movements (sound-powering movements). The sound may acquire additional structure via upper tract movements, such as articulatory movements or head raising (sound-filtering movements). Some movements are not necessary for sound production, but when produced, impinge on the sound-producing process due to weak biomechanical coupling with body parts (e.g., respiratory system) that are necessary for sound production (sound-impinging movements). Animals also produce sounds contingent with movement, requiring neuro-physiological control regimes allowing to flexibly couple movements to a produced sound, or coupling movements to a perceived external sound (sound-contingent movement). Here, we compare and classify the variety of ways sound and movements are coupled in animal communication; our proposed framework should help structure previous and future studies on this topic.

Through the extended evolutionary meta-model, and what ACT found there: ACT as a process-based therapy.

This article is part of a special issue in the Journal of Contextual Behavioral Science devoted to process-based therapy (PBT) or a process-based approach to therapy and the role it plays in harmonizing existing evidence-based treatments. In the present discussion, we focus on acceptance and commitment therapy (ACT) and how it fits into the PBT framework. We describe how viewing ACT through a PBT framework and its organizing rubric-the extended evolutionary meta-model (EEMM)-provides fertile ground to expand the ACT and psychological flexibility models, transforming ACT into a more inclusive and flexible version of itself and giving clinicians wider berth with respect to delivering ACT. The PBT approach allows ACT to incorporate therapeutic elements that are not traditionally part of the framework, including include cognitive reappraisal, interpersonal therapy dynamics, physiological downregulation, and the principle of nonattachment. Importantly, ACT maintains its foundational principles throughout this integration. We provide a case example of how to use PBT methods to conceptualize an ACT case, to illustrate PBT-infused ACT in practice. Finally, we outline possible future directions for ACT as it continues to evolve inside of PBT.

The potential for evolutionary rescue in an Arctic seashore plant threatened by climate change.

The impacts of climate change may be particularly severe for geographically isolated populations, which must adjust through plastic responses or evolve. Here, we study an endangered Arctic plant, Primula nutans ssp. finmarchica, confined to Fennoscandian seashores and showing indications of maladaptation to warming climate. We evaluate the potential of these populations to evolve to facilitate survival in the rapidly warming Arctic (i.e. evolutionary rescue) by utilizing manual crossing experiments in a nested half-sibling breeding design. We estimate G-matrices, evolvability and genetic constraints in traits with potentially conflicting selection pressures. To explicitly evaluate the potential for climate change adaptation, we infer the expected time to evolve from a northern to a southern phenotype under different selection scenarios, using demographic and climatic data to relate expected evolutionary rates to projected rates of climate change. Our results indicate that, given the nearly 10-fold greater evolvability of vegetative than of floral traits, adaptation in these traits may take place nearly in concert with changing climate, given effective climate mitigation. However, the comparatively slow expected evolutionary modification of floral traits may hamper the evolution of floral traits to track climate-induced changes in pollination environment, compromising sexual reproduction and thus reducing the likelihood of evolutionary rescue.

Multiple origins of dorsal ecdysial sutures in trilobites and their relatives.

Euarthropods are an extremely diverse phylum in the modern, and have been since their origination in the early Palaeozoic. They grow through moulting the exoskeleton (ecdysis) facilitated by breaking along lines of weakness (sutures). Artiopodans, a group that includes trilobites and their non-biomineralizing relatives, dominated arthropod diversity in benthic communities during the Palaeozoic. Most trilobites - a hyperdiverse group of tens of thousands of species - moult by breaking the exoskeleton along cephalic sutures, a strategy that has contributed to their high diversity during the Palaeozoic. However, the recent description of similar sutures in early diverging non-trilobite artiopodans means that it is unclear whether these sutures evolved deep within Artiopoda, or convergently appeared multiple times within the group. Here, we describe new well-preserved material of Acanthomeridion, a putative early diverging artiopodan, including hitherto unknown details of its ventral anatomy and appendages revealed through CT scanning, highlighting additional possible homologous features between the ventral plates of this taxon and trilobite free cheeks. We used three coding strategies treating ventral plates as homologous to trilobite-free cheeks, to trilobite cephalic doublure, or independently derived. If ventral plates are considered homologous to free cheeks, Acanthomeridion is recovered sister to trilobites, however, dorsal ecdysial sutures are still recovered at many places within Artiopoda. If ventral plates are considered homologous to doublure or non-homologous, then Acanthomeridion is not recovered as sister to trilobites, and thus the ventral plates represent a distinct feature to trilobite doublure/free cheeks.

Evolutionary game analysis of multi-agent cooperation strategy analysis in agricultural water conservancy PPP project under digitization background.

This study constructed a system dynamics (SD) evolutionary game model, from which we analyzed the conditions that need to be met by multi-agents to achieve cooperation in agricultural water conservancy (AWC) Public-Private Partnership (PPP) projects. Simultaneously, this study used numerical simulation to depict the impact of initial participation willingness, reward, punishment, income, and other parameters of local governments, project enterprises, and farmers on the evolution strategy. The results firstly showed that the basic conditions for tripartite cooperation are to strengthen the rewards and punishments for project enterprises and improve the participation benefits of farmers. Secondly, it showed that increasing the rewards for farmers and improving the project dividend benefits can effectively motivate farmers to participate. Thirdly, it showed that enhancing the incentive subsidies and reputation benefits for project enterprise's participation, as well as strengthening the supervision of local government, boosts the motivation of project enterprises significantly. However, it is vital to ensure that the reward amounts are not too high to allow local government to play its guiding role efficiently. Lastly, the results indicated that digital technology can help reduce the participation cost of local government, project enterprises and farmers, and improve the external reputation benefits of each participant, thus forming a win-win situation for all three participants. Analyzing the cooperation strategies of the three parties in AWC PPP projects provides theoretical support and a decision-making basis for the efficient operation of AWC PPP projects under the digital background, and also provides practical policy recommendations for the high-quality development of AWC.

Genomic signatures of positive selection in Awarik dromedary camels from southwestern of Saudi Arabia.

Introduction: The Awarik camel population in southwestern Saudi Arabia exhibits unique genetic and phenotypic traits compared to other domestic camel populations. This study aims to explore the genomic signatures of positive selection in Awarik camels to understand their evolutionary history and identify genetic adaptations potentially shared with East African camel populations. Methods: Whole genome sequencing data from nine Awarik camels were analyzed using two robust intra-population haplotype-based statistical methods: integrated haplotype score (iHS) and number of segregating sites by length (nSL). These analyses were conducted to identify candidate regions under positive selection within the Awarik camel genome. Results and discussion: These analyses identified 66 and 53 candidate selection regions, encompassing 185 and 123 genes, respectively. The iHS analysis revealed significant selection signals on chromosomes 15 and 16, including a robust overlap on chromosome 15 (10 regions) involving the TRNAI-AAU gene, suggesting its critical role in adaptive processes. Additionally, chromosome 3 exhibited the highest number of candidate regions totaling 10. The nSL analysis highlighted statistically significant regions on chromosomes 2 and 7, as well as a high concentration of candidate regions on chromosome 14, totaling five regions. Notably, large candidate regions were also identified on chromosome 11 (200 kb: 51.750-51.950 kb) and chromosome 9 (325 kb: 45.825-46.150 kb). Functional annotation of these genes revealed involvement in diverse biological processes including olfactory activity, immune regulation, metabolism, insulin secretion, reproductive performance, kidney function, and cellular signaling, with specific genes like BAG5, septin 7, SLC13A1, PCED1B, BMPR1B, ZAR1, JAKMIP2, and NOTCH2 highlighted. These findings contribute to our understanding of the adaptive mechanisms of Awarik camels and have important implications for breeding and conservation strategies. Further research on these genetic adaptations, particularly those affecting immune response, is crucial to mitigate the impacts of climate change on camel populations.

Warmer is better for evolutionary rescue, driving a warm-to-cold bias in habitat colonization dynamics.

Evolutionary rescue occurs when populations survive lethal environmental stresses through the rising and fixation of tolerant genotypes. Temperature has long been believed to determine the evolutionary speed of populations and species. Here, we suggest that warmer temperatures can facilitate evolutionary rescue. Moreover, with dispersal among habitats, the advantage in evolutionary rescue for warmer populations may cause a bias in habitat colonization dynamics towards the warm-to-cold direction. We experimentally tested these hypotheses with a model microbial system. Our first experiment showed that bacterial populations at warmer temperatures had a greater chance to evolve resistance and escape the fate of extinction under an antibiotic treatment. In the second experiment, metapopulations that consisted of warm and cold habitats were exposed to the antibiotic stress; local populations that went extinct might be recolonized, and such recolonization events were biased to the warm-to-cold direction. We also examined possible mechanisms underlying the temperature effect on the rapid evolution of resistance in our study system. Our results may help to understand the mechanisms of maintenance of biodiversity and patterns of gene flow among climatic regions, particularly in pest species subject to chemical control treatments.

Genomic surveillance reveals low-level circulation of two subtypes of genogroup C coxsackievirus A10 in Nanchang, Jiangxi Province, China, 2015-2023.

Introduction: In recent years, coxsackievirus (CV) A10 has been associated with increasing sporadic hand, foot, and mouth disease (HFMD) cases and outbreaks globally. In addition to mild symptoms such as pharyngitis and herpangina, CVA10-related complications or even fatality can occur. Currently, systematic phylogenetic studies of CVA10 are limited. Methods: In this study, we first explored the epidemiological and genetic characteristics of CVA10 in Nanchang, an inland southeastern city of China, based on the HFMD surveillance network from 2015-2023. Results: Among 3429 enterovirus-positive cases, 110 (3.04%) were associated with CVA10, with a male-to-female ratio of 1.62. The median age of the CVA10 patients was 2.3 years (interquartile range, IQR 1.0-4.0), with 94.55% (104/110) of the patients aged less than 5 years. Phylogenetic analyses using the full-length VP1, 5'UTR, P1, P2, P3 sequences and near full-length genomes indicated that CVA10 strains (n = 57) isolated in Nanchang belonged to genogroup C; two strains identified in 2017 belonged to C1 subtypes clustered with strains from Vietnam, Madagascar, France and Spain; and the others belonged to C2 subtypes interdigitating with CVA10 isolates from mainland China, the United States and Australia. Through extensive analysis, we identified a rare F168Y mutation in epitope 4 of VP1 in a Madagascar strain of genogroup F and a Chinese strain of genogroup C. Based on Bayesian evolutionary analyses, the average nucleotide substitution rate for the VP1 gene of CV10 strains was 3.07×10-3 substitutions/site/year. The most recent common ancestor (tMRCA) of genogroup C was dated 1990.84, and the tMRCA of CVA10 strains from Nanchang was dated approximately 2003.16, similar to strains circulating in other regions of China, suggesting that the viruses were likely introduced and cryptically circulated in China before the establishment of the HFMD surveillance network. Recombination analysis indicated intertypic recombination of the Nanchang strain with the genogroup G strain in the 3D region. Discussion: Given the shifting dominance of viral genotypes and frequent recombination events, the existing surveillance system needs to be regulated to enhance genomic surveillance efforts on a more diverse spectrum of genotypes in the future.

Whole genomes from the extinct Xerces Blue butterfly can help identify declining insect species.

The Xerces Blue (Glaucopsyche xerces) is considered to be the first butterfly to become extinct in historical times. It was notable for its chalky lavender wings with conspicuous white spots on the ventral wings. The last individuals were collected in their restricted habitat, in the dunes near the Presidio military base in San Francisco, in 1941. We sequenced the genomes of four 80- to 100-year-old Xerces Blue, and seven historical and one modern specimens of its closest relative, the Silvery Blue (Glaucopsyche lygdamus). We compared these to a novel annotated genome of the Green-Underside Blue (Glaucopsyche alexis). Phylogenetic relationships inferred from complete mitochondrial genomes indicate that Xerces Blue was a distinct species that diverged from the Silvery Blue lineage at least 850,000 years ago. Using nuclear genomes, both species experienced population growth during the Eemian interglacial period, but the Xerces Blue decreased to a very low effective population size subsequently, a trend opposite to that observed in the Silvery Blue. Runs of homozygosity and deleterious load in the former were significantly greater than in the later, suggesting a higher incidence of inbreeding. These signals of population decline observed in Xerces Blue could be used to identify and monitor other insects threatened by human activities, whose extinction patterns are still not well known.

Characterizing the role of unpredictability within different dimensions of early life adversity.

Dimensional models of early life adversity highlight the distinct roles of deprivation and threat in shaping neurocognitive development and mental health. However, relatively little is known about the role of unpredictability within each dimension. We estimated both the average levels of, and the temporal unpredictability of deprivation and threat exposure during adolescence in a high-risk, longitudinal sample of 1354 youth (Pathways to Desistance study). We then related these estimates to later life psychological distress, and Antisocial and Borderline personality traits, and tested whether any effects are mediated by future orientation. High average levels of both deprivation and threat exposure were found to be associated with worse mental health on all three outcomes, but only the effects on Antisocial and Borderline personality traits were mediated by decreased future orientation, a pattern consistent with evolutionary models of psychopathology. Unpredictability in deprivation exposure proved to be associated with increased psychological distress and a higher number of Borderline traits, but with increased future orientation. There was some evidence of unpredictability in threat exposure buffering against the detrimental developmental effects of average threat levels. Our results suggest that the effects of unpredictability are distinct within different dimensions of early life adversity.

Genomic epidemiology describes introduction and outbreaks of antifungal drug-resistant Candida auris.

Candida auris is a globally emerged fungal pathogen causing nosocomial invasive infections. Here, we use cutting-edge genomic approaches to elucidate the temporal and geographic epidemiology of drug-resistant C. auris within the UK. We analysed a representative sample of over 200 isolates from multiple UK hospitals to assess the number and timings of C. auris introductions and infer subsequent patterns of inter- and intra-hospital transmission of azole drug-resistant isolates. We identify at least one introduction from Clade I and two from Clade III into the UK, and observe temporal and geographical evidence for multiple transmission events of antifungal drug resistant isolates between hospitals and identified local within-hospital patient-to-patient transmission events. Our study confirms outbreaks of drug-resistant C. auris are linked and that transmission amongst patients occurs, explaining local hospital outbreaks, and demonstrating a need for improved epidemiological surveillance of C. auris to protect patients and healthcare services.

Development and experimental validation of computational methods for human antibody affinity enhancement.

High affinity is crucial for the efficacy and specificity of antibody. Due to involving high-throughput screens, biological experiments for antibody affinity maturation are time-consuming and have a low success rate. Precise computational-assisted antibody design promises to accelerate this process, but there is still a lack of effective computational methods capable of pinpointing beneficial mutations within the complementarity-determining region (CDR) of antibodies. Moreover, random mutations often lead to challenges in antibody expression and immunogenicity. In this study, to enhance the affinity of a human antibody against avian influenza virus, a CDR library was constructed and evolutionary information was acquired through sequence alignment to restrict the mutation positions and types. Concurrently, a statistical potential methodology was developed based on amino acid interactions between antibodies and antigens to calculate potential affinity-enhanced antibodies, which were further subjected to molecular dynamics simulations. Subsequently, experimental validation confirmed that a point mutation enhancing 2.5-fold affinity was obtained from 10 designs, resulting in the antibody affinity of 2 nM. A predictive model for antibody-antigen interactions based on the binding interface was also developed, achieving an Area Under the Curve (AUC) of 0.83 and a precision of 0.89 on the test set. Lastly, a novel approach involving combinations of affinity-enhancing mutations and an iterative mutation optimization scheme similar to the Monte Carlo method were proposed. This study presents computational methods that rapidly and accurately enhance antibody affinity, addressing issues related to antibody expression and immunogenicity.

Navigating the personal carbon inclusion scheme: An evolutionary game theory approach to low-carbon behaviors among socio-economic groups.

In response to the challenges posed by climate change, China has launched the Personal Carbon Inclusion (PCI) scheme to encourage individuals to transition towards low-carbon lifestyles. This study investigates the behaviors of participants within the PCI scheme using a tripartite evolutionary game model, encompassing high-income and low-income individuals, as well as the PCI platform itself. The research analyzes participants' strategies, examines the evolutionary stability of different strategies, and assesses the robustness of equilibrium points within the game dynamics. Key findings reveal: (1) High-income participants tend to be less willing to adopt low-carbon behaviors compared to low-income participants. (2) The PCI platform displays limited proactive engagement in promoting low-carbon policies. (3) Factors, particularly the cost and pricing mechanisms of low-carbon actions, significantly impact the evolutionary progression of the system. Moreover, practical recommendations are provided to enhance the effectiveness of PCI schemes.

A comprehensive survey of evolutionary algorithms and metaheuristics in brain EEG-based applications.

Electroencephalography (EEG) has emerged as a primary non-invasive and mobile modality for understanding the complex workings of the human brain, providing invaluable insights into cognitive processes, neurological disorders, and brain-computer interfaces (BCI). Nevertheless, the volume of EEG data, the presence of artifacts, the selection of optimal channels, and the need for feature extraction from EEG data present considerable challenges in achieving meaningful and distinguishing outcomes for machine learning algorithms utilized to process EEG data. Consequently, the demand for sophisticated optimization techniques has become imperative to overcome these hurdles effectively. Evolutionary algorithms (EAs) and other nature-inspired metaheuristics have been applied as powerful design and optimization tools in recent years, showcasing their significance in addressing various design and optimization problems relevant to brain EEG based applications. This paper presents a comprehensive survey highlighting the importance of EAs and other metaheuristics in EEG-based applications. The survey is organized according to the main areas where EAs have been applied, namely artifact mitigation, channel selection, feature extraction, feature selection, and signal classification. Finally, the current challenges and future aspects of EAs in the context of EEG-based applications are discussed.

Inter and intra-island genetic structure and differentiation of the endemic Bolle's Laurel Pigeon (Columba bollii) in the Canary archipelago.

Islands provide excellent settings for studying the evolutionary history of species, since their geographic isolation and relatively small size limit gene flow between populations, and promote divergence and speciation. The endemic Bolle's Laurel Pigeon Columba bollii is an arboreal frugivorous bird species distributed on laurel forests in four islands of the Canary archipelago. To elucidate the population genetics, we genotyped ten microsatellite loci using DNA obtained from non-invasive samples collected across practically all laurel forest remnants, and subsequently grouped into eight sampling sites. Analyses including F-statistics, Bayesian clustering approaches, isolation by distance tests and population graph topologies, were used to infer the genetic diversity and the population differentiation within and among insular populations. Additionally, we evaluated the effect of null alleles on data analysis. Low genetic diversity was found in all populations of Bolle's Laurel Pigeon, with no significant differences in diversity among them. However, significant genetic differentiation was detected among all populations, with pigeons from La Palma and El Hierro exhibiting the closest affinity. Bayesian clustering supported population separation between islands, and also detected fine-scale structure within the Tenerife and La Gomera populations. Our results suggest that, despite columbids have a high movement ability, they can show signature of genetic divergence among populations, particularly on oceanic islands. Geological history of the islands and distribution range of habitats could have close influence on the evolutionary trajectories of these birds. This approach can provide practical tools to implement appropriate conservation measures for range-restricted species and their habitat.

A novel multi-objective dung beetle optimizer for Multi-UAV cooperative path planning.

Path planning for multiple unmanned aerial vehicles (UAVs) is crucial in collaborative operations and is commonly regarded as a complicated, multi-objective optimization problem. However, traditional approaches have difficulty balancing convergence and diversity, as well as effectively handling constraints. In this study, a directional evolutionary non-dominated sorting dung beetle optimizer with adaptive stochastic ranking (DENSDBO-ASR) is developed to address these issues in collaborative multi-UAV path planning. Two objectives are initially formulated: the first one represents the total cost of length and altitude, while the second represents the total cost of threat and time. Additionally, an improved multi-objective dung beetle optimizer is introduced, which integrates a directional evolutionary strategy including directional mutation and crossover, thereby accelerating convergence and enhancing global search capability. Furthermore, an adaptive stochastic ranking mechanism is proposed to successfully handle different constraints by dynamically adjusting the comparison probability. The effectiveness and superiority of DENSDBO-ASR are demonstrated by the constrained problem functions (CF) test, the Wilcoxon rank sum test, and the Friedman test. Finally, three sets of simulated tests are carried out, each including different numbers of UAVs. In the most challenging scenario, DENSDBO-ASR successfully identifies feasible paths with average values of the two objective functions as low as 637.26 and 0. The comparative results demonstrate that DENSDBO-ASR outperforms the other five algorithms in terms of convergence accuracy and population diversity, making it an exceptional optimization approach to path planning challenges.

Digital economy and the medical and health service supply in China.

The impact of the digital economy on the healthcare sector is becoming increasingly profound. This article focuses on the relationship between the development of China's digital economy and medical and health services supply. Based on panel data from 30 provinces in China from 2012 to 2021, the CRITIC weight method was applied to measure the supply capacity of medical and health services and the level of digital economy development, and the kernel density estimation method and Dagum Gini coefficient method was used to characterize the evolutionary trends and regional differences. Additionally, a two-way fixed-effects model is adopted to investigate the impact of digital economy development on medical and health services supply. The results show that both the supply capacity of healthcare services and the level of digital economy development have been increasing continuously in terms of evolutionary trends. From the perspective of regional differences, compared to the supply level of healthcare services, the regional differences in digital economy development are more significant. The intra-regional differences in medical and health services supply are greater than the inter-regional differences, while the development of the digital economy exhibits the opposite trend. The findings of this paper provide supports for China to enhance the development level of digital economy and improve supply of medical and health service.

Genomic insights into adaptive evolution of the species-rich cosmopolitan plant genus Rhododendron.

The species-rich cosmopolitan genus Rhododendron offers a good system for exploring the genomic mechanisms underlying adaptation to diverse habitats. Here, we report high-quality chromosomal-level genome assemblies of nine species, representing all five subgenera, different altitudinal distributions, and all flower color types of this genus. Further comprehensive genomic analyses indicate diverse adaptive strategies employed by Rhododendron, particularly adaptation to alpine and subalpine habitats by expansion/contraction of gene families involved in pathogen defense and oxidative phosphorylation, genomic convergent evolution, and gene copy-number variation. The convergent adaptation to high altitudes is further shown by population genomic analysis of R. nivale from the Himalaya-Hengduan Mountains. Moreover, we identify the genes involved in the biosynthesis of anthocyanins and carotenoids, which play a crucial role in shaping flower color diversity and environmental adaptation. Our study is significant for comprehending plant adaptive evolution and the uneven distribution of species diversity across different geographical regions.

Structural and Evolutionary Analysis of Proteins Endowed with a Nucleotidyltransferase, or Non-canonical Palm, Catalytic Domain.

Many polymerases and other proteins are endowed with a catalytic domain belonging to the nucleotidyltransferase fold, which has also been deemed the non-canonical palm domain, in which three conserved acidic residues coordinate two divalent metal ions. Tertiary structure-based evolutionary analyses provide valuable information when the phylogenetic signal contained in the primary structure is blurry or has been lost, as is the case with these proteins. Pairwise structural comparisons of proteins with a nucleotidyltransferase fold were performed in the PDBefold web server: the RMSD, the number of superimposed residues, and the Qscore were obtained. The structural alignment score (RMSD × 100/number of superimposed residues) and the 1-Qscore were calculated, and distance matrices were constructed, from which a dendogram and a phylogenetic network were drawn for each score. The dendograms and the phylogenetic networks display well-defined clades, reflecting high levels of structural conservation within each clade, not mirrored by primary sequence. The conserved structural core between all these proteins consists of the catalytic nucleotidyltransferase fold, which is surrounded by different functional domains. Hence, many of the clades include proteins that bind different substrates or partake in non-related functions. Enzymes endowed with a nucleotidyltransferase fold are present in all domains of life, and participate in essential cellular and viral functions, which suggests that this domain is very ancient. Despite the loss of evolutionary traces in their primary structure, tertiary structure-based analyses allow us to delve into the evolution and functional diversification of the NT fold.

Deciphering the role of FOXP4 in long COVID: exploring genetic associations, evolutionary conservation, and drug identification through bioinformatics analysis.

Long COVID (LC) refers to a condition characterized by a variety of lingering symptoms that persist for more than 4 to 12 weeks following the initial acute SARS-CoV-2 infection. Recent research has suggested that the FOXP4 gene could potentially be a significant factor contributing to LC. Owing to that, this study investigates FOXP4's role in LC by analyzing public datasets to understand its evolution and expression in diverse human populations and searching for drugs to reduce LC symptoms. Population genetic analysis of FOXP4 across human populations unmasks distinct genetic diversity patterns and positive selection signatures, suggesting potential population-specific susceptibilities to conditions like LC. Further, we also observed that FOXP4 experiences high expression during LC. To identify potential inhibitors, drug screening analysis identifies synthetic drugs like Glisoxepide, and natural compounds Kapurimycin A3 produced from Streptomyces sp, and Cucurbitacin B from Begonia nantoensis as promising candidates. Overall, our research contributes to understanding how FOXP4 may serve as a therapeutic target for mitigating the impact of LC.