Detecting localized damage in cantilevered structures under nonstationary ambient excitations via Gabor spectral mode transmissibility functions.

A method based on Gabor spectral mode transmissibility functions (GSMTFs) is proposed to detect local damage in a cantilevered structure under nonstationary ambient excitations. Gabor transformation and singular value decomposition are used to reduce the influences of other vibration modes on Gabor spectral mode transmissibility functions and process nonstationary structural responses, respectively. A new state characteristic based on the fundamental structure frequency is formulated on the basis of the GSMTFs, eventually leading to the development of a new damage indicator. The probability density functions of the damage indicator for healthy and damaged states can be estimated from the measured data, and the receiver operating characteristic (ROC) curve derived from these probability distributions and the corresponding area under the ROC curve (AUC) are used to determine the damage location. A six-degree-of-freedom system and a typical transmission tower are numerically studied, and the results show that the proposed method can estimate the structural damage location under nonstationary random loads. The proposed method is further validated with a planar frame in the laboratory, which exhibits multiple damage elements via random force hammer excitations. The results show that the AUC values computed for certain parts of the structure containing the damaged elements are greater than those for other parts of the structure, indicating the effectiveness of the proposed method. Moreover, the proposed method is compared with the dot product difference (DPD) index, and the results from the laboratory planar frame demonstrate that the proposed method can better identify damage.

Replication kinetics and infectivity of SARS-CoV-2 Omicron variant sublineages recovered in the Republic of Korea.

BACKGROUND: We analyzed the correlation between the infectivity and transmissibility of the severe acute respiratory syndrome coronavirus 2 Omicron sublineages BA.1, BA. 2, BA.4, and BA.5. METHODS: We assessed viral replication kinetics and infectivity at the cellular level. Nasopharyngeal and oropharyngeal specimens were obtained from patients with coronavirus disease 2019, confirmed using whole-genome sequencing to be caused by the Omicron sublineages BA.1, BA.2, BA.4, or BA.5. These specimens were used to infect Vero E6 cells, derived from monkey kidneys, for the purpose of viral isolation. Viral stocks were then passaged in Vero E6 cells at a multiplicity of infection of 0.01, and culture supernatants were harvested at 12-hour intervals for 72 hours. To evaluate viral replication kinetics, we determined the cycle threshold values of the supernatants using real-time reverse transcription polymerase chain reaction and converted these values to genome copy numbers. RESULTS: The viral load was comparable between BA.2, BA.4, and BA.5, whereas BA.1 exhibited a lower value. The peak infectious load of BA.4 was approximately 3 times lower than that of BA.2 and BA.5, while the peak load of BA.2 and BA.5 was about 7 times higher than that of BA.1. Notably, BA.1 demonstrated the lowest infectivity over the entire study period. CONCLUSION: Our results suggest that the global BA.5 wave may have been amplified by the higher viral replication and infectivity of BA.5 compared to other Omicron sublineages. These analyses could support the rapid assessment of the impact of novel variants on case incidence.

Mathematical modeling of SARS-CoV-2 variant substitutions in European countries: transmission dynamics and epidemiological insights.

Background: Countries across Europe have faced similar evolutions of SARS-CoV-2 variants of concern, including the Alpha, Delta, and Omicron variants. Materials and methods: We used data from GISAID and applied a robust, automated mathematical substitution model to study the dynamics of COVID-19 variants in Europe over a period of more than 2 years, from late 2020 to early 2023. This model identifies variant substitution patterns and distinguishes between residual and dominant behavior. We used weekly sequencing data from 19 European countries to estimate the increase in transmissibility ( Δ ß ) between consecutive SARS-CoV-2 variants. In addition, we focused on large countries with separate regional outbreaks and complex scenarios of multiple competing variants. Results: Our model accurately reproduced the observed substitution patterns between the Alpha, Delta, and Omicron major variants. We estimated the daily variant prevalence and calculated Δ ß between variants, revealing that: ( i ) Δ ß increased progressively from the Alpha to the Omicron variant; ( i i ) Δ ß showed a high degree of variability within Omicron variants; ( i i i ) a higher Δ ß was associated with a later emergence of the variant within a country; ( i v ) a higher degree of immunization of the population against previous variants was associated with a higher Δ ß for the Delta variant; ( v ) larger countries exhibited smaller Δ ß , suggesting regionally diverse outbreaks within the same country; and finally ( v i ) the model reliably captures the dynamics of competing variants, even in complex scenarios. Conclusion: The use of mathematical models allows for precise and reliable estimation of daily cases of each variant. By quantifying Δ ß , we have tracked the spread of the different variants across Europe, highlighting a robust increase in transmissibility trend from Alpha to Omicron. Additionally, we have shown that the geographical characteristics of a country, as well as the timing of new variant entrances, can explain some of the observed differences in variant substitution dynamics across countries.

A speed limit on serial strain replacement from original antigenic sin.

Many pathogens evolve to escape immunity, yet it remains difficult to predict whether immune pressure will lead to diversification, serial replacement of one variant by another, or more complex patterns. Pathogen strain dynamics are mediated by cross-protective immunity, whereby exposure to one strain partially protects against infection by antigenically diverged strains. There is growing evidence that this protection is influenced by early exposures, a phenomenon referred to as original antigenic sin (OAS) or imprinting. In this paper, we derive constraints on the emergence of the pattern of successive strain replacements demonstrated by influenza, SARS-CoV-2, seasonal coronaviruses, and other pathogens. We find that OAS implies that the limited diversity found with successive strain replacement can only be maintained if [Formula: see text] is less than a threshold set by the characteristic antigenic distances for cross-protection and for the creation of new immune memory. This bound implies a "speed limit" on the evolution of new strains and a minimum variance of the distribution of infecting strains in antigenic space at any time. To carry out this analysis, we develop a theoretical model of pathogen evolution in antigenic space that implements OAS by decoupling the antigenic distances required for protection from infection and strain-specific memory creation. Our results demonstrate that OAS can play an integral role in the emergence of strain structure from host immune dynamics, preventing highly transmissible pathogens from maintaining serial strain replacement without diversification.

Multiscale selection in spatially structured populations.

The spatial structure of populations is key to many (eco-)evolutionary processes. In such cases, the strength and sign of selection on a trait may depend on the spatial scale considered. An example is the evolution of altruism: selection in local environments often favours cheaters over altruists, but this can be outweighed by selection at larger scales, favouring clusters of altruists over clusters of cheaters. For populations subdivided into distinct groups, this effect is described formally by multilevel selection theory. However, many populations do not consist of non-overlapping groups but rather (self-)organize into other ecological patterns. We therefore present a mathematical framework for multiscale selection. This framework decomposes natural selection into two parts: local selection, acting within environments of a certain size, and interlocal selection, acting among them. Varying the size of the local environments subsequently allows one to measure the contribution to selection of each spatial scale. To illustrate the use of this framework, we apply it to models of the evolution of altruism and pathogen transmissibility. The analysis identifies how and to what extent ecological processes at different spatial scales contribute to selection and compete, thus providing a rigorous underpinning to eco-evolutionary intuitions.

Enhancing infectious diseases early warning: A deep learning approach for influenza surveillance in China.

Objective: This study aimed to develop a universally applicable, feedback-informed Self-Excitation Attention Residual Network (SEAR) model. This model dynamically adapts to evolving disease trends and surveillance system changes, accommodating various scenarios. Thereby enhancing the effectiveness of early warning systems. Methods: Surveillance data on influenza-like illness (ILI) was collected from various regions including Northern China, Southern China, Beijing, and Yunnan. The reproduction number (Rt) was estimated to determine the threshold for issuing warnings. The Self-Excitation Attention Residual Network (SEAR) was devised employing deep learning algorithms and was trained, validated, and tested. The SEAR model's efficacy was assessed based on five metrics: accuracy rate, recall rate, F1 score, confusion matrix, and the receiver operating characteristic curve. Results: With an advance warning set at three days, the SEAR model outperformed five primary models - logistic regression, support vector machine, random forest, Extreme Gradient Boosting, and Long Short-Term Memory model - in all five evaluation metrics. Notably, the model's warning performance declined with an increase in the early warning value and the number of warning days, albeit maintaining a ROC value over 0.7 in all scenarios. Conclusion: The SEAR model demonstrated robust early warning performance for influenza in diverse Chinese regions with high accuracy and specificity. This novel model, augmenting traditional systems, supports widespread application for respiratory disease outbreak monitoring. Future evaluations could incorporate alternative indicators, with the model continuously updating through data feedback, thus enhancing its universal applicability. Ongoing optimization, using iterative feedback and expert judgment, heralds a transformative approach to surveillance-based early warning strategies.

Transmissibility of Mycobacterium pinnipedii in a murine model.

The causative agent of tuberculosis in pinnipeds is Mycobacterium pinnipedii, a member of the Mycobacterium tuberculosis complex (MTC). The natural hosts are pinnipeds; however, other non-marine mammals, including humans, can also be infected. The transmissibility of a pathogen is related to its virulence. The transmissibility of a M. pinnipedii strain (i.e., 1856) was investigated in a murine model and compared with that of two Mycobacterium bovis strains (i.e., 534 and 04-303) with different reported virulence. Non-inoculated mice (sentinels) were co-housed with intratracheally inoculated mice. Detailed inspection of mice to search for visible tuberculosis lesions in the lungs and spleen was performed, and bacillus viability at 30, 60, and 90 days post-inoculation (dpi) was assayed. A transmissibility of 100% was recorded at 30 dpi in sentinel mice co-housed with the inoculated mice from the M. pinnipedii and M. bovis 04-303 groups, as evidenced by the recovery of viable M. pinnipedii and M. bovis from the lungs of sentinel mice. Mice inoculated with M. pinnipedii (1856) and M. bovis (534) survived until euthanized, whereas five of the M. bovis 04-303-inoculated mice died at 17 dpi. This study constitutes the first report of the transmissibility of a M. pinnipedii strain in mice and confirms the utility of this experimental model to study virulence features such as the transmission of poorly characterized MTC species.

Study of the Prediction of Vibrations in Soft Soil Foundations Based on Field Tests.

To explore the prediction of vibrations in soft soil foundations, in light of the construction of laboratories with microvibration requirements on soft soil foundations which are subject to the limitations of urban land planning, field testing was conducted, and the soil surface vibration responses were recorded at different distances from a road under various highway traffic loads. By analyzing the data which summarize the characteristics of soft soil foundations, it is clarified that the vibration response of soft soil foundations mainly occurs at low frequencies, and the vibration response under road traffic loads is prone to resonance at the natural frequency of soft soil foundations. Subsequently, a new vibration prediction method based on the vibration transmission ratio is proposed, and its effectiveness and accuracy based on transmissibility are verified. This research study provides a reference for laboratories constructed on soft soil and for surrounding traffic planning.

A biomechanical model of a vehicle passenger in the sagittal plane.

Musculoskeletal biomechanical models have wide applications in ergonomics, rehabilitation, and injury estimation. Their use can be extended to enable quantitatively explaining and estimating ride comfort for a vehicle's passenger. A biomechanical model of the upper body in the sagittal plane is constructed, which allows for curved motion to simulate the propagation of disturbance energy within a seated passenger aboard a moving vehicle. The dynamic predictions of the model are validated against experimental results within the literature. Frequency responses show that within the vehicular frequency range, the L4L5 and the L5S1 discs in the lower lumbar region are susceptible to the highest vibration transmission. It was also found that vibration transmission is maximized at around 4.5 Hz. The model provides analytical and geometric intuition into the motion of the various segments of the upper body using a few simple geometric assumptions and can be employed to develop a quantitative ride-comfort metric, such that the most comfortable ride would be that which would induce the least internal motion within the passenger model.

Molecular insights into the adaptive evolution of SARS-CoV-2 spike protein.

The COVID-19 pandemic, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has substantially damaged the global economy and human health. The spike (S) protein of coronaviruses plays a pivotal role in viral entry by binding to host cell receptors. Additionally, it acts as the primary target for neutralizing antibodies in those infected and is the central focus for currently utilized or researched vaccines. During the virus's adaptation to the human host, the S protein of SARS-CoV-2 has undergone significant evolution. As the COVID-19 pandemic has unfolded, new mutations have arisen and vanished, giving rise to distinctive amino acid profiles within variant of concern strains of SARS-CoV-2. Notably, many of these changes in the S protein have been positively selected, leading to substantial alterations in viral characteristics, such as heightened transmissibility and immune evasion capabilities. This review aims to provide an overview of our current understanding of the structural implications associated with key amino acid changes in the S protein of SARS-CoV-2. These research findings shed light on the intricate and dynamic nature of viral evolution, underscoring the importance of continuous monitoring and analysis of viral genomes. Through these molecular-level investigations, we can attain deeper insights into the virus's adaptive evolution, offering valuable guidance for designing vaccines and developing antiviral drugs to combat the ever-evolving viral threats.

Is It Possible to Test for Viral Infectiousness?: The Use Case of (SARS-CoV-2).

Identifying and managing individuals with active or chronic disease, implementing appropriate infection control measures, and mitigating the spread of the COVID-19 pandemic highlighted the need for tests of infectiousness. The gold standard for assessing infectiousness has been the recovery of infectious virus in cell culture. Using cycle threshold values, antigen testing, and SARS-CoV-2, replication intermediate strands were used to assess infectiousness, with many limitations. Infectiousness can be influenced by host factors (eg, preexisting immune responses) and virus factors (eg, evolution).

Transmission of fore-and-aft floor vibration to the spine and head of standing people.

Sixteen standing male participants were subjected to fore-and-aft sinusoidal vibration with peak magnitude and frequency in the range 0.44-4.431 ms-2 and 2-6 Hz, respectively. The fore-and-aft, lateral and vertical transmissibilities to the first dorsal vertebra (T1), eighth dorsal vertebra (T8), twelfth dorsal vertebra (T12), fourth lumbar vertebra (L4) and head were measured. Large inter-participant variability was observed in the transmissibilities at all locations. Nevertheless, peaks in the range 3-4.5 Hz were identified at all locations, implying a whole-body resonance in this frequency range. The response was found dominant in the mid-sagittal plane as the lateral transmissibility showed low values. Below 4.5 Hz, the fore-and-aft transmissibility increased with moving from caudal to cranial locations of the upper body. However, at higher frequencies, the opposite trend was observed. The results can be used for developing models that may help understand how vibration affects health and comfort.

Systems Biology Approaches to Understanding COVID-19 Spread in the Population.

In essence, the COVID-19 pandemic can be regarded as a systems biology problem, with the entire world as the system, and the human population as the element transitioning from one state to another with certain transition rates. While capturing all the relevant features of such a complex system is hardly possible, compartmental epidemiological models can be used as an appropriate simplification to model the system's dynamics and infer its important characteristics, such as basic and effective reproductive numbers of the virus. These measures can later be used as response variables in feature selection methods to uncover the main factors contributing to disease transmissibility. We here demonstrate that a combination of dynamic modeling and machine learning approaches can represent a powerful tool in understanding the spread, not only of COVID-19, but of any infectious disease of epidemiological proportions.

Experimental Infection of Chickens with H5N8 High Pathogenicity Avian Influenza Viruses Isolated in Japan in the Winter of 2020-2021.

During the winter of 2020-2021, numerous outbreaks of high pathogenicity avian influenza (HPAI) were caused by viruses of the subtype H5N8 in poultry over a wide region in Japan. The virus can be divided into five genotypes-E1, E2, E3, E5, and E7. The major genotype responsible for the outbreaks was E3, followed by E2. To investigate the cause of these outbreaks, we experimentally infected chickens with five representative strains of each genotype. We found that the 50% chicken infectious dose differed by up to 75 times among the five strains, and the titer of the E3 strains (102.75 50% egg infectious dose (EID50)) was the lowest, followed by that of the E2 strains (103.50 EID50). In viral transmission experiments, in addition to the E3 and E2 strains, the E5 strain was transmitted to naïve chickens with high efficiency (>80%), whereas the other strains had low efficiencies (<20%). We observed a clear difference in the virological characteristics among the five strains isolated in the same season. The higher infectivity of the E3 and E2 viruses in chickens may have caused the large number of HPAI outbreaks in Japan during this season.

Characterization of a SARS-CoV-2 EG.5.1 clinical isolate in vitro and in vivo.

EG.5.1 is a subvariant of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron XBB variant that is rapidly increasing in prevalence worldwide. However, the pathogenicity, transmissibility, and immune evasion properties of isolates of EG.5.1 are largely unknown. Here, we show that there are no obvious differences in growth ability and pathogenicity between EG.5.1 and XBB.1.5 in hamsters. We also demonstrate that, like XBB.1.5, EG.5.1 is transmitted more efficiently between hamsters compared to its predecessor, BA.2. In contrast, unlike XBB.1.5, we detect EG.5.1 in the lungs of four of six exposed hamsters, suggesting that the virus properties of EG.5.1 are different from those of XBB.1.5. Finally, we find that the neutralizing activity of plasma from convalescent individuals against EG.5.1 was slightly, but significantly, lower than that against XBB.1.5 or XBB.1.9.2. Our data suggest that the different virus properties after transmission and the altered antigenicity of EG.5.1 may be driving its increasing prevalence over XBB.1.5 in humans.

Effects of Distance-Learning Strategies in Dental Fixed Prosthodontics Amidst the COVID-19 Pandemic: Cross-Sectional Questionnaire Study on Preclinical Dental Students' Perspective.

BACKGROUND: COVID-19's high transmissibility led to gathering restrictions where dental schools experienced disruptions due to restrictions on attending in-person lectures and limitations placed on applied preclinical and clinical activities. Students not only had to rapidly switch to digital technology-based learning (TB-learning) modules but also experienced high levels of social isolation and anxiety around virus transmission. OBJECTIVE: This study aims to evaluate the preclinical students' perception of switching TB-learning modules amidst the COVID-19 pandemic, identifying which module parameters were associated with strong student outcomes. METHODS: A web-based survey of 39 Likert scale questions was delivered to preclinical dental students (Western University) to evaluate students' perceptions concerning TB-learning, fear amidst the COVID-19 pandemic, and the impact on their preclinical training. A Spearman rank correlation coefficient was determined to estimate the relationship between 2 variables in isolation (P=.01). An ordinal regression analysis was performed on variables of interest to determine how module variables (typically within the instructor's control) influenced the student outcomes (P=.05). RESULTS: The response rate was 30% (n=39). TB-learning was considered vital (34/39, 87.2%) as the students' education improved (18/39, 46.2%). However, 53.8% (n=21) of students showed increased difficulties in retaining, visualizing, or understanding the materials using TB-learning, and 64.1% (n=25) found it more difficult to concentrate than in in-person classes. In total, 79.5% (n=31) of students showed different levels of agreement about feeling fatigued from TB-learning. Through Spearman ρ correlation analysis, the quality of questions in quizzes (ρ=0.514; P<.001), relevant handouts (ρ=0.729; P<.001), and high-quality audiovisuals (ρ=0.585; P<.001) were positively correlated with students responding that the modules were useful to preclinical training. Similarly, good organization (ρ=0.512; P<.001), high-quality questions in quizzes (ρ=0.431; P=.01), and relevant handouts (ρ=0.551; P<.001) were positively correlated with web-based classes as an effective way to learn. In total, 91.6% (n=36) of the students agreed that COVID-19 was a dangerous disease, whereas 53.8% (n=21) showed different levels of agreement that they were afraid to be infected personally, and 69.2% (n=27) feared passing COVID-19 along to family and friends. A total of 82.1% (n=32) of the students showed that COVID-19 impacted their overall learning process and had a negative impact on their practical preclinical training (31/39, 79.5%). CONCLUSIONS: The students found a difference between TB-learning and face-to-face learning methods, where the students perceived fatigue toward the web-based method with difficulty concentrating and visualizing the subject. Moreover, there was a consensus that COVID-19 itself affected the students' overall learning process and preclinical training. As dental schools continue implementing TB-learning into their curriculum, this investigation identifies the students' struggles with the paradigm shift. In an effort to improve TB-learning, this work highlights 4 variables (organization, quizzes, quality handouts, and quality audiovisuals) within the control of instructors that can help improve content deliverance, improving the students' experience.

Association of Ambient Temperature and Absolute Humidity with the Effective Reproduction Number of COVID-19 in Japan.

This study aimed to quantify the exposure-lag-response relationship between short-term changes in ambient temperature and absolute humidity and the transmission dynamics of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in Japan. The prefecture-specific daily time-series of newly confirmed cases, meteorological variables, retail and recreation mobility, and Government Stringency Index were collected for all 47 prefectures of Japan for the study period from 15 February 2020 to 15 October 2022. Generalized conditional Gamma regression models were formulated with distributed lag nonlinear models by adopting the case-time-series design to assess the independent and interactive effects of ambient temperature and absolute humidity on the relative risk (RR) of the time-varying effective reproductive number (Rt). With reference to 17.8 °C, the corresponding cumulative RRs (95% confidence interval) at a mean ambient temperatures of 5.1 °C and 27.9 °C were 1.027 (1.016-1.038) and 0.982 (0.974-0.989), respectively, whereas those at an absolute humidity of 4.2 m/g3 and 20.6 m/g3 were 1.026 (1.017-1.036) and 0.995 (0.985-1.006), respectively, with reference to 10.6 m/g3. Both extremely hot and humid conditions synergistically and slightly reduced the Rt. Our findings provide a better understanding of how meteorological drivers shape the complex heterogeneous dynamics of SARS-CoV-2 in Japan.

In Vivo and In Vitro Characterization of the Recently Emergent PRRSV 1-4-4 L1C Variant (L1C.5) in Comparison with Other PRRSV-2 Lineage 1 Isolates.

The recently emerged PRRSV 1-4-4 L1C variant (L1C.5) was in vivo and in vitro characterized in this study in comparison with three other contemporary 1-4-4 isolates (L1C.1, L1A, and L1H) and one 1-7-4 L1A isolate. Seventy-two 3-week-old PRRSV-naive pigs were divided into six groups with twelve pigs/group. Forty-eight pigs (eight/group) were for inoculation, and 24 pigs (four/group) served as contact pigs. Pigs in pen A of each room were inoculated with the corresponding virus or negative media. At two days post inoculation (DPI), contact pigs were added to pen B adjacent to pen A in each room. Pigs were necropsied at 10 and 28 DPI. Compared to other virus-inoculated groups, the L1C.5-inoculated pigs exhibited more severe anorexia and lethargy, higher mortality, a higher fraction of pigs with fever (>40 °C), higher average temperature at several DPIs, and higher viremia levels at 2 DPI. A higher percentage of the contact pigs in the L1C.5 group became viremic at two days post contact, implying the higher transmissibility of this virus strain. It was also found that some PRRSV isolates caused brain infection in inoculation pigs and/or contact pigs. The complete genome sequences and growth characteristics in ZMAC cells of five PRRSV-2 isolates were further compared. Collectively, this study confirms that the PRRSV 1-4-4 L1C variant (L1C.5) is highly virulent with potential higher transmissibility, but the genetic determinants of virulence remain to be elucidated.

A state-of-the-art review on biomechanical models and biodynamic responses.

Biomechanical models are mathematical representations of human structure. These models are used to analyse joint and injury mechanics and design of prosthetic devices for human body under various conditions. Biomechanical model development involves the integration of knowledge from various fields, including mechanics, biology, physiology, and mathematics. Biomechanical models have become more significant in the healthcare sector as researchers strive to offer better medical supplies and ride comfort. It has uses in automobile and sports science as well, to create human dummies for accident and segmental vibration transmissibility study, improve training routines, and prevent injuries. These biomechanical models might be anything from straightforward lumped parameter models to intricate multi-body models. The virtues, weaknesses, and contemporary uses of lumped parameter modelling and multi body modelling in biomechanical modelling are discussed in this article. Subsequently, emphasised the recent modelling improvements and explored the future direction of biomechanical modelling. Researchers and professionals who wish to apply biomechanical models to comprehend human movement and enhance performance may find this review to be helpful.

Our understanding of how the human body functions, moves, and responds to various situations has greatly improved as a result of the current review. The models play a critical role in the simulation and quantification of interactions between anatomical structures, tissues, and external forces, providing essential information on mobility, function, and damage mechanisms.

Mutations in HA and PA affect the transmissibility of H7N9 avian influenza virus in chickens.

Low pathogenic (LP) H7N9 avian influenza virus (AIV) emerged in 2013 and had spread widely over several months in China, experienced a noteworthy reduction in isolation rate in poultry and human since 2017. Here, we examined the transmission of H7N9 viruses to better understand viral spread and dissemination mechanisms. Three out of four viruses (2013-2016) could transmit in chickens through direct contact, and airborne transmission was confirmed in the JT157 (2016) virus. However, we did not detect the transmission of the two 2017 viruses, WF69 and AH395, through either direct or airborne exposure. Molecular analysis of genome sequence of two viruses identified eleven mutations located in viral proteins (except for matrix protein), such as PA (K362R and S364N) and HA (D167N, H7 numbering), etc. We explored the genetic determinants that contributed to the difference in transmissibility of the viruses in chickens by generating a series of reassortants in the JT157 background. We found that the replacement of HA gene in JT157 by that of WF69 abrogated the airborne transmission in recipient chickens, whereas the combination of HA and PA replacement led to the loss of airborne and direct contact transmission. Failure with contact transmission of the viruses has been associated with the emergence of the mutations D167N in HA and K362R and S364N in PA. Furthermore, the HA D167N mutation significantly reduced viral attachment to chicken lung and trachea tissues, while mutations K362R and S364N in PA reduced the nuclear transport efficiency and the PA protein expression levels in both cytoplasm and nucleus of CEF cells. The D167N substitution in HA reduced the H7N9 viral acid stability and avian-like receptor binding, while enhanced human-like receptor binding. Further analysis revealed these mutants grew poorly in vitro and in vivo. To conclude, H7N9 AIVs that contain mutations in the HA and PA protein reduced the viral transmissibility in chicken, and may pose a reduced threat for poultry but remain a heightened public health risk.