Background: Chronic hepatitis C (HCV) infection presents global health challenges with significant morbidity and mortality implications. Successfully treating patients with cirrhosis may lead to mortality rates comparable to the general population. This study aims to utilize machine learning techniques to create predictive mortality models for individuals with chronic HCV infections. Methods: Data from chronic HCV patients at Sultan Qaboos University Hospital (2009–2017) underwent analysis. Data pre-processing handled missing values and scaled features using Python via Anaconda. Model training involved SelectKBest feature selection and algorithms such as logistic regression, random forest, gradient boosting, and SVM. The evaluation included diverse metrics, with 5-fold cross-validation, ensuring consistent performance assessment. Results: A cohort of 702 patients meeting the eligibility criteria, predominantly male, with a median age of 47, was analyzed across a follow-up period of 97.4 months. Survival probabilities at 12, 36, and 120 months were 90.0%, 84.0%, and 73.0%, respectively. Ten key features selected for mortality prediction included hemoglobin levels, alanine aminotransferase, comorbidities, HCV genotype, coinfections, follow-up duration, and treatment response. Machine learning models, including the logistic regression, random forest, gradient boosting, and support vector machine models, showed high discriminatory power, with logistic regression consistently achieving an AUC value of 0.929. Factors associated with increased mortality risk included cardiovascular diseases, coinfections, and failure to achieve a SVR, while lower ALT levels and specific HCV genotypes were linked to better survival outcomes. Conclusions: This study presents the use of machine learning models to predict mortality in chronic HCV patients, providing crucial insights for risk assessment and tailored treatments. Further validation and refinement of these models are essential to enhance their clinical utility, optimize patient care, and improve outcomes for individuals with chronic HCV infections. Full article

The widely adopted paradigm in brain–computer interfaces (BCIs) involves motor imagery (MI), enabling improved communication between humans and machines. EEG signals derived from MI present several challenges due to their inherent characteristics, which lead to a complex process of classifying and finding the potential tasks of a specific participant. Another issue is that BCI systems can result in noisy data and redundant channels, which in turn can lead to increased equipment and computational costs. To address these problems, the optimal channel selection of a multiclass MI classification based on a Fusion convolutional neural network with Attention blocks (FCNNA) is proposed. In this study, we developed a CNN model consisting of layers of convolutional blocks with multiple spatial and temporal filters. These filters are designed specifically to capture the distribution and relationships of signal features across different electrode locations, as well as to analyze the evolution of these features over time. Following these layers, a Convolutional Block Attention Module (CBAM) is used to, further, enhance EEG signal feature extraction. In the process of channel selection, the genetic algorithm is used to select the optimal set of channels using a new technique to deliver fixed as well as variable channels for all participants. The proposed methodology is validated showing 6.41% improvement in multiclass classification compared to most baseline models. Notably, we achieved the highest results of 93.09% for binary classes involving left-hand and right-hand movements. In addition, the cross-subject strategy for multiclass classification yielded an impressive accuracy of 68.87%. Following channel selection, multiclass classification accuracy was enhanced, reaching 84.53%. Overall, our experiments illustrated the efficiency of the proposed EEG MI model in both channel selection and classification, showing superior results with either a full channel set or a reduced number of channels. Full article

The mechanobiological response mechanism of the fenestrae of liver sinusoidal endothelial cells (LSECs) to the physical stiffness of the extracellular matrix (ECM) remains unclear. We investigated how the mechanical properties of their substrates affect the LSECs’ fenestrae by the nitric oxide (NO)-dependent pathway and how they relate to the progression of hepatic sinus capillarization during liver fibrosis. We detected different stiffnesses of ECM in the progress of liver fibrosis (LF) and developed polyacrylamide hydrogel (PAM) substrates to simulate them. Softer stiffness substrates contributed to LSECs maintaining fenestrae phenotype in vitro. The stiffness of liver fibrosis tissue could be reversed in vivo via treatment with anti-ECM deposition drugs. Similarly, the capillarization of LSECs could be reversed by decreasing the ECM stiffness. Our results also indicate that the NO-dependent pathway plays a key regulatory role in the capillarization of ECM-LSECs. Our study reveals ECM-induced mechanotransduction of capillarized LSECs through a NO-dependent pathway via a previously unrevealed mechanotransduction mechanism. The elucidation of this mechanism may offer precise biomechanics-specific intervention strategies targeting liver fibrosis progression. Full article

Cuticle proteins (CPs) constitute a multifunctional family; however, the physiological role of Cuticle Protein 3-like (CP3L) in Heortia vitessoides Moore remains largely unclear. In this study, we cloned the HvCP3L gene from the transcriptional library of Heortia vitessoides Moore. RT-qPCR results revealed that HvCP3L exhibited high expression levels during the larval stage of Heortia vitessoides Moore, particularly at the L5D1 stage, observed in both larval and adult heads. Through RNA interference, we successfully silenced the HvCP3L gene, resulting in a significant reduction in the survival rate of Heortia vitessoides Moore, with the survival rate from larvae to adults plummeting to a mere 17.7%, accompanied by phenotypic abnormalities. Additionally, we observed that the knockdown of HvCP3L led to the inhibition of genes in the chitin pathway. Following exposure to methoxyfenozide stress, the HvCP3L gene exhibited significant overexpression, coinciding with phenotypic abnormalities. These findings underscore the pivotal role of HvCP3L in the growth and development of Heortia vitessoides Moore. Full article

Sustainability of biological, social, and economic systems is crucial for protecting our common future and preserving the balance between nature and humans. Environmental concerns should be adopted by all units of society and sustainability awareness should be adapted to all processes through optimum technologies both in daily life and in business management. The basic objective of this article is to determine the effects of green core competencies, green process innovation, and firm performance variables on each other and to examine the moderating role of sustainability consciousness on these effects. A survey and semi-structured interview forms were preferred as data collection methods. In the analysis of the survey data, AMOS was adopted to test the hypothetical model and the Hayes Process macro was employed to determine the moderating effect. The data of interview forms were analyzed with the bag of words model. The research results show that green core competencies positively affect green process innovation and green process innovation positively affects firm performance. In addition, the moderating effect of the attitudinal and behavioral dimensions of sustainability awareness on the impact of green process innovation on firm performance is supported, while the moderating effect of the sustainability knowingness dimension is not supported. Full article

Coral reef health is tightly connected to the coral holobiont, which is the association between the coral animal and a diverse microbiome functioning as a unit. The coral holobiont depends on key services such as nitrogen and sulfur cycling mediated by the associated bacteria. However, these microbial services may be impaired in response to environmental changes, such as thermal stress. A perturbed microbiome may lead to coral bleaching and disease outbreaks, which have caused an unprecedented loss in coral cover worldwide, particularly correlated to a warming ocean. The response mechanisms of the coral holobiont under high temperatures are not completely understood, but the associated microbial community is a potential source of acquired heat-tolerance. Here we investigate the effects of increased temperature on the taxonomic and functional profiles of coral surface mucous layer (SML) microbiomes in relationship to coral–algal physiology. We used shotgun metagenomics in an experimental setting to understand the dynamics of microbial taxa and genes in the SML microbiome of the coral Pseudodiploria strigosa under heat treatment. The metagenomes of corals exposed to heat showed high similarity at the level of bacterial genera and functional genes related to nitrogen and sulfur metabolism and stress response. The coral SML microbiome responded to heat with an increase in the relative abundance of taxa with probiotic potential, and functional genes for nitrogen and sulfur acquisition. Coral–algal physiology significantly explained the variation in the microbiome at taxonomic and functional levels. These consistent and specific microbial taxa and gene functions that significantly increased in proportional abundance in corals exposed to heat are potentially beneficial to coral health and thermal resistance. Full article

Time series forecasting has been a challenging area in the field of Artificial Intelligence. Various approaches such as linear neural networks, recurrent linear neural networks, Convolutional Neural Networks, and recently transformers have been attempted for the time series forecasting domain. Although transformer-based architectures have been outstanding in the Natural Language Processing domain, especially in autoregressive language modeling, the initial attempts to use transformers in the time series arena have met mixed success. A recent important work indicating simple linear networks outperform transformer-based designs. We investigate this paradox in detail comparing the linear neural network- and transformer-based designs, providing insights into why a certain approach may be better for a particular type of problem. We also improve upon the recently proposed simple linear neural network-based architecture by using dual pipelines with batch normalization and reversible instance normalization. Our enhanced architecture outperforms all existing architectures for time series forecasting on a majority of the popular benchmarks. Full article

Gait analysis plays important roles in various applications such as exercise therapy, biometrics, and robot control. It can also be used to prevent and improve movement disorders and monitor health conditions. We implemented a wearable module equipped with an MPU-9250 IMU sensor, and Bluetooth modules were implemented on an Arduino Uno R3 board for gait analysis. Gait cycles were identified based on roll values measured by the accelerometer embedded in the IMU sensor. By superimposing the gait cycles that occurred during the walking period, they could be analyzed using statistical methods. We found that the subjects could be identified using the gait feature points extracted through the statistical modeling process. To validate the feasibility of feature-based gait pattern identification, we constructed various machine learning models and compared the accuracy of their gait pattern identification. Based on this, we also investigated whether there was a significant difference between the gait patterns of people who used cell phones while walking and those who did not. Full article

In our modern society, there has been a noticeable increase in pollution due to the trend of post-use handling of items. This necessitates the adoption of recycling and remanufacturing processes, advocating for sustainable resource management. This paper aims to address the issue of disassembly line balancing. Existing disassembly methods largely rely on manual labor, raising concerns regarding safety and sustainability. This paper proposes a human–machine collaborative disassembly approach to enhance safety and optimize resource utilization, aligning with sustainable development goals. A mixed-integer programming model is established, considering various disassembly techniques for hazardous and delicate parts, with the objective of minimizing the total disassembly time. The CPLEX solver is employed to enhance model accuracy. An improvement is made to the Q-learning algorithm in reinforcement learning to tackle the bilateral disassembly line balancing problem in human–machine collaboration. This approach outperforms CPLEX in both solution efficiency and quality, especially for large-scale problems. A comparative analysis with the original Q-learning algorithm and SARSA algorithm validates the superiority of the proposed algorithm in terms of convergence speed and solution quality. Full article

To clarify the hydrodynamic interference characteristics of flows around multiple cylinders under the wall effect, the two-dimensional (2D) flows around the near-wall single, two tandem and parallel cylinders are simulated under different gap ratios (0.15 ≤ G/D ≤ 3.0) and spacing ratios (1.5 ≤ T/D ≤ 4.0) at a Reynolds number of Re = 6300. We also examine the wake patterns, the force coefficients, and the vortex-shedding frequency with emphases on the wall effect and effects of the two-cylinder interference. A critical wall gap of G/D = 0.6 is identified in the single-cylinder case where the wall can exert significant influences. The two near-wall tandem cylinders exhibit three wake states: stretching mode, attachment mode, and impinging mode. The force coefficients on the upstream cylinder are significantly affected by the wall for G/D ≤ 0.6. The downstream cylinder is mainly influenced by the upstream cylinder. For G/D > 0.6, the force coefficients on the two cylinders exhibit a similar variation trend. In the parallel arrangement, the two cylinders exhibit four wake states in different G/D and T/D ranges: double stretching mode, hetero-vortex scale mode, unilateral vortex mode, and free vortex mode. Moreover, the two parallel cylinders in the hetero-vortex scale or free vortex mode have two states: synchronous in-phase state and synchronous out-of-phase state. The mean drag coefficients on the two cylinders decrease, while the mean lift coefficients exhibit opposite variation trends, as the T/D grows. Full article

In the process of collecting evidence of activities and events in network devices, there are problems with content and storage, and we aim to solve the problems faced by network devices in network forensics. In this paper, we propose a simple method for solving the problems with content and storage in acquiring communication logs on end hosts, implement a sniffing tool that captures raw packets with communication event control, compare it with existing tools, and conduct experiments and considerations. Through these experiments and considerations, we confirmed that the proposed communication log acquisition method can be implemented on the end host, and that the problem can be solved by using a tool that implements the proposed method. Also, we confirmed that it can be applied to real-world communication log collection scenarios, and that it can coexist with existing systems and tools that collect communication logs. Full article

Excitotoxicity is a common pathological process in neurological diseases caused by excess glutamate. The purpose of this study was to evaluate the effect of gypenoside XVII (GP-17), a gypenoside monomer, on the glutamatergic system. In vitro, in rat cortical nerve terminals (synaptosomes), GP-17 dose-dependently decreased glutamate release with an IC₅₀ value of 16 μM. The removal of extracellular Ca²⁺ or blockade of N-and P/Q-type Ca²⁺ channels and protein kinase A (PKA) abolished the inhibitory effect of GP-17 on glutamate release from cortical synaptosomes. GP-17 also significantly reduced the phosphorylation of PKA, SNAP-25, and synapsin I in cortical synaptosomes. In an in vivo rat model of glutamate excitotoxicity induced by kainic acid (KA), GP-17 pretreatment significantly prevented seizures and rescued neuronal cell injury and glutamate elevation in the cortex. GP-17 pretreatment decreased the expression levels of sodium-coupled neutral amino acid transporter 1, glutamate synthesis enzyme glutaminase and vesicular glutamate transporter 1 but increased the expression level of glutamate metabolism enzyme glutamate dehydrogenase in the cortex of KA-treated rats. In addition, the KA-induced alterations in the N-methyl-D-aspartate receptor subunits GluN2A and GluN2B in the cortex were prevented by GP-17 pretreatment. GP-17 also prevented the KA-induced decrease in cerebral blood flow and arginase II expression. These results suggest that (i) GP-17, through the suppression of N- and P/Q-type Ca²⁺ channels and consequent PKA-mediated SNAP-25 and synapsin I phosphorylation, reduces glutamate exocytosis from cortical synaptosomes; and (ii) GP-17 has a neuroprotective effect on KA-induced glutamate excitotoxicity in rats through regulating synaptic glutamate release and cerebral blood flow. Full article

Background: Chronic obstructive pulmonary disease (COPD) has significant systemic manifestations, including cardiovascular morbidity. The main aim of our study was to evaluate the effect of short-term COPD treatment with tiotropium/olodaterol (TIO/OLO) 5/5 μg on cardiac function and autonomic integrity. Methods: Twenty-nine patients with newly diagnosed moderate-to-severe COPD were enrolled. We performed pulmonary function tests, cardiac magnetic resonance, cardiac 123I-metaiodobenzylguanidine (123I-MIBG) imaging and analysis of blood biomarkers on our study subjects. The correlations between the tests’ results were evaluated at baseline. The changes in pulmonary and cardiac parameters from baseline through 12 weeks were assessed. Results: Significant associations between pulmonary function tests’ results and high-sensitivity C-reactive protein (hs-CRP), as well as interleukin-22 (IL-22), were observed at baseline. Treatment with TIO/OLO significantly improved lung function as measured by spirometry and body plethysmography. Moreover, we found that the cardiac index increased from 2.89 (interquartile range (IQR) 1.09) to 3.21 L/min/m² (IQR 0.78) (p = 0.013; N = 18) and the late heart-to-mediastinum ratio improved from 1.88 (IQR 0.37) to 2 (IQR 0.41) (p = 0.026; N = 16) after 12 weeks of treatment. Conclusions: Treatment with TIO/OLO improves lung function and positively impacts cardiac function and autonomic integrity, suggesting that dual bronchodilation might have a potential in decreasing the risk for cardiac events in COPD. Hs-CRP and IL-22 might be beneficial in determining the intensity of systemic inflammation in COPD. Further research with a larger cohort is needed to enhance the initial results of this study. Full article

Rhabdoid meningiomas (RM) are a rare meningioma subtype with a heterogeneous clinical course which is more frequently associated with recurrence, even among tumors undergoing-complete surgical removal. Here, we retrospectively analyzed the clinical-histopathological and cytogenetic features of 29 tumors, from patients with recurrent (seven primary and 14 recurrent tumors) vs. non-recurrent RM (n = 8). Recurrent RM showed one (29%), two (29%) or three (42%) recurrences. BAP1 loss of expression was found in one third of all RM at diagnosis and increased to 100% in subsequent tumor recurrences. Despite both recurrent and non-recurrent RM shared chromosome 22 losses, non-recurrent tumors more frequently displayed extensive losses of chromosome 19p (62%) and/or 19q (50%), together with gains of chromosomes 20 and 21 (38%, respectively), whereas recurrent RM (at diagnosis) displayed more complex genotypic profiles with extensive losses of chromosomes 1p, 14q, 18p, 18q (67% each) and 21p (50%), together with focal gains at chromosome 17q22 (67%). Compared to paired primary tumors, recurrent RM samples revealed additional losses at chromosomes 16q and 19p (50% each), together with gains at chromosomes 1q and 17q in most recurrent tumors (67%, each). All deceased recurrent RM patients corresponded to women with chromosome 17q gains, although no statistical significant differences were found vs. the other RM patients. Full article

Yeast two-hybrid approaches, which are based on fusion proteins that must co-localise to the nucleus to reconstitute the transcriptional activity of GAL4, have greatly contributed to our understanding of the nitrogen interaction network of cyanobacteria, the main hubs of which are the trimeric PII and the monomeric PipX regulators. The bacterial two-hybrid system, based on the reconstitution in the E. coli cytoplasm of the adenylate cyclase of Bordetella pertussis, should provide a relatively faster and presumably more physiological assay for cyanobacterial proteins than the yeast system. Here, we used the bacterial two-hybrid system to gain additional insights into the cyanobacterial PipX interaction network while simultaneously assessing the advantages and limitations of the two most popular two-hybrid systems. A comprehensive mutational analysis of PipX and bacterial two-hybrid assays were performed to compare the outcomes between yeast and bacterial systems. We detected interactions that were previously recorded in the yeast two-hybrid system as negative, as well as a “false positive”, the self-interaction of PipX, which is rather an indirect interaction that is dependent on PII homologues from the E. coli host, a result confirmed by Western blot analysis with relevant PipX variants. This is, to our knowledge, the first report of the molecular basis of a false positive in the bacterial two-hybrid system. Full article

The paper is devoted to the comparative analysis of different CFD techniques used to solve the problem of high-pressure hydrogen release into the air. Three variations of a contemporary low-dissipation numerical technique (CABARET) are compared with each other and a conventional first-order numerical scheme. It is shown that low dissipation of the numerical scheme defines better resolution of the contact surface between released hydrogen and ambient air. As a result, the spatial structures of the jet and the reaction wave that arise during self-ignition are better resolved, which is useful for predicting the local effects of high-pressure hydrogen release. At the same time, the dissipation has little effect on the induction delay, so critical conditions of self-ignition can be reliably reproduced even via conventional numerical schemes. The test problem setups formulated in the paper can be used as benchmarks for compressible CFD solvers. Full article

Perception-based fidelity evaluation metrics are crucial in driving simulators, as they play a key role in the automatic tuning, assessment, and comparison of motion cueing algorithms. Nevertheless, there is presently no unified and effective evaluation framework for these algorithms. To tackle this challenge, our study initially establishes a model rooted in visual–vestibular interaction and head tilt angle perception systems. We then employ metrics like the Normalized Average Absolute Difference (NAAD), Normalized Pearson Correlation (NPC), and Estimated Delay (ED) to devise an evaluation index system. Furthermore, we use a combined approach incorporating CRITIC and gray relational analysis to ascertain the weights of these indicators. This allows us to consolidate them into a comprehensive evaluation metric that reflects the overall fidelity of motion cueing algorithms. Subjective evaluation experiments validate the reasonableness and efficacy of our proposed Perception Fidelity Evaluation (PFE) method. Full article

Chemotherapy is one of the most commonly used methods for treating cancer, but its side effects severely limit its application and impair treatment effectiveness. Removing off-target chemotherapy drugs from the serum promptly through adsorption is the most direct approach to minimize their side effects. In this study, we synthesized a series of adsorption materials to remove the chemotherapy drug doxorubicin by modifying MOF nanosheets with sulfonated azocalix[4]arenes. The strong affinity of sulfonated azocalix[4]arenes for doxorubicin results in high adsorption strength (Langmuir adsorption constant = 2.45–5.73 L mg⁻¹) and more complete removal of the drug. The extensive external surface area of the 2D nanosheets facilitates the exposure of a large number of accessible adsorption sites, which capture DOX molecules without internal diffusion, leading to a high adsorption rate (pseudo-second-order rate constant = 0.0058–0.0065 g mg⁻¹ min⁻¹). These adsorbents perform effectively in physiological environments and exhibit low cytotoxicity and good hemocompatibility. These features make them suitable for removing doxorubicin from serum during “drug capture” procedures. The optimal adsorbent can remove 91% of the clinical concentration of doxorubicin within 5 min. Full article

Background: In patients with embolic stroke of undetermined source (ESUS), occult atrial fibrillation (AF) has been implicated as a key source of cardioembolism. However, only a minority acquire implantable cardiac loop recorders (ILRs) to detect occult paroxysmal AF, partly due to financial cost and procedural inconvenience. Without the initiation of appropriate anticoagulation, these patients are at risk of increased ischemic stroke recurrence. Hence, cost-effective and accurate methods of predicting AF in ESUS patients are highly sought after. Objective: We aimed to incorporate clinical and echocardiography data into machine learning (ML) algorithms for AF prediction on ILRs in ESUS. Methods: This was a single-center cohort study that included 157 consecutive patients diagnosed with ESUS from October 2014 to October 2017 who had ILR evaluation. We developed four ML models, with hyperparameters tuned, to predict AF detection on an ILR. Results: The median age of the cohort was 67 (IQR 59–74) years old and the median monitoring duration was 1051 (IQR 478–1287) days. Of the 157 patients, 32 (20.4%) had occult AF detected on the ILR. Support vector machine predicted for AF with a 95% confidence interval area under the receiver operating characteristic curve (AUC) of 0.736–0.737, multilayer perceptron with an AUC of 0.697–0.708, XGBoost with an AUC of 0.697–0.697, and random forest with an AUC of 0.663–0.674. ML feature importance found that age, HDL-C, and admitting heart rate were important non-echocardiography variables, while peak mitral A-wave velocity and left atrial volume were important echocardiography parameters aiding this prediction. Conclusion: Machine learning modeling incorporating clinical and echocardiographic variables predicted AF in ESUS patients with moderate accuracy. Full article

Inconel 625 is a nickel-based superalloy widely used in industries such as energy, space, and defence, due to its strength and corrosion resistance. It is traditionally time- and resource-intensive to machine, leading to increased environmental impact and material waste. Using additive manufacturing (AM) technology enables a reduction in resource consumption during the manufacture of high value components, as material is only deposited where it is required. This study compares the environmental impact of manufacturing an Inconel 625 impeller through machining and wire arc additive manufacturing (WAAM) by employing established life cycle assessment methods. WAAM shows significant advantages, cutting energy consumption threefold and reducing material waste from 85% to 35%. The current work also evaluates the mechanical properties of WAAM-produced components through tensile and axial fatigue testing, in addition to the use of optical and electron microscopy for metallurgical analysis and fractography. This demonstrates yield and ultimate tensile strengths exceeding industrial standards, with comparable or superior fatigue life to other AM methods. The improved fatigue performance extends the service life of components, bolstering sustainability by reducing the need for frequent replacements, thereby lessening associated environmental impacts. These findings underscore the promise of WAAM in enhancing both environmental sustainability and mechanical performance in manufacturing Inconel 625 components. Full article

The porcine reproductive and respiratory syndrome virus (PRRSV) has significantly impacted the global pork industry for over three decades. Its high mutation rates and frequent recombination greatly intensifies its epidemic and threat. To explore the fidelity characterization of Chinese highly pathogenic PRRSV JXwn06 and the NADC30-like strain CHsx1401, self-recombination and mutation in PAMs, MARC-145 cells, and pigs were assessed. In vitro, CHsx1401 displayed a higher frequency of recombination junctions and a greater diversity of junction types than JXwn06. In vivo, CHsx1401 exhibited fewer junction types yet maintained a higher junction frequency. Notably, JXwn06 showed more accumulation of mutations. To pinpoint the genomic regions influencing their fidelity, chimeric viruses were constructed, with the exchanged nsp9-10 regions between JXwn06 and CHsx1401. The SJn9n10 strain, which incorporates JXwn06’s nsp9-10 into the CHsx1401 genome, demonstrated reduced sensitivity to nucleotide analogs compared to CHsx1401. Conversely, compared with JXwn06, the JSn9n10 strain showed increased sensitivity to these inhibitors. The swapped nsp9-10 also influences the junction frequency and accumulated mutations as their donor strains. The results indicate a propensity for different types of genetic variations between these two strains and further highlight the nsp9-10 region as a critical determinant of their fidelity. Full article

Avian reovirus (ARV) infection can cause significant losses to the poultry industry. Disease control has traditionally been attempted mainly through vaccination. However, the increase in clinical outbreaks in the last decades demonstrated the poor effectiveness of current vaccination approaches. The present study reconstructs the evolution and molecular epidemiology of different ARV genotypes using a phylodynamic approach, benefiting from a collection of more than one thousand sigma C (σC) sequences sampled over time at a worldwide level. ARVs’ origin was estimated to occur several centuries ago, largely predating the first clinical reports. The origins of all genotypes were inferred at least one century ago, and their emergence and rise reflect the intensification of the poultry industry. The introduction of vaccinations had only limited and transitory effects on viral circulation and further expansion was observed, particularly after the 1990s, likely because of the limited immunity and the suboptimal and patchy vaccination application. In parallel, strong selective pressures acted with different strengths and directionalities among genotypes, leading to the emergence of new variants. While preventing the spread of new variants with different phenotypic features would be pivotal, a phylogeographic analysis revealed an intricate network of viral migrations occurring even over long distances and reflecting well-established socio-economic relationships. Full article