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African swine fever virus (ASFV) is a large, double-stranded DNA virus that causes a fatal disease in pigs, posing a threat to the global pig industry. Whereas some ASFV proteins have been found to play important roles in ASFV–host interaction, the functional roles of many proteins are still largely unknown. In this study, we identified I73R , an early viral gene in the replication cycle of ASFV, as a key virulence factor. Our findings demonstrate that pI73R suppresses the host innate immune response by broadly inhibiting the synthesis of host proteins, including antiviral proteins. Crystallization and structural characterization results suggest that pI73R is a nucleic-acid-binding protein containing a Zα domain. It localizes in the nucleus and inhibits host protein synthesis by suppressing the nuclear export of cellular messenger RNA (mRNAs). While pI73R promotes viral replication, the deletion of the gene showed that it is a nonessential gene for virus replication. In vivo safety and immunogenicity evaluation results demonstrate that the deletion mutant ASFV-GZΔ I73R is completely nonpathogenic and provides effective protection to pigs against wild-type ASFV. These results reveal I73R as a virulence-related gene critical for ASFV pathogenesis and suggest that it is a potential target for virus attenuation. Accordingly, the deletion mutant ASFV-GZΔ I73R can be a potent live-attenuated vaccine candidate.

African swine fever (ASF) is a highly contagious disease of domestic and wild pigs caused by the African swine fever virus (ASFV). The current research on ASF vaccines focuses on the development of naturally attenuated, isolated, or genetically engineered live viruses that have been demonstrated to produce reliable immunity. As a result, a genetically engineered virus containing five genes deletion was synthesized based on ASFV Chinese strain GZ201801, named ASFV-GZΔI177LΔCD2vΔMGF. The five-gene-deleted ASFV was safe and fully attenuated in pigs and provides reliable protection against the parental ASFV strain challenge. This indicates that the five-gene-deleted ASFV is a potential candidate for a live attenuated vaccine that could control the spread of ASFV.

In this paper, we study the endomorphism properties of vertex operator algebras over an arbitrary field $\mathbb{F}$, with $\text{Char}(\mathbb{F}) \neq 2$. Let $V$ be a strongly finitely generated vertex operator algebra over $\mathbb{F}$, and $M$ be an irreducible admissible $V$-module. We prove that every element in $\text{End}_V(M)$ is algebraic over $\mathbb{F}$ and that $\text{End}_V(M)$ is also finite-dimensional. As an application, we prove Schur's lemma for strongly finitely generated vertex operator algebras over arbitrary algebraically closed fields, and we give a test for absolute irreducibility of $V$-modules.

The catalytic reduction performance of NO on the surface of Fe-doped ZnAl2O4 (100) was calculated based on DFT. The adsorption of NO and other molecules, the change of reaction energy of CH4 and C2H4 as reducing agents, and the activation energy barrier of CH4 were studied. It was found that the best adsorption energy of NO is −2.166 eV. Compared with Al and Zn sites, doped Fe atoms are better adsorption catalytic sites. At temperatures of 300 K and 600 K, the molecules will move in the direction of the Fe atoms. O2 adsorption will repel NO, reduce its adsorption energy, and cause NO to lose electrons and be oxidized. The reaction enthalpy with CH4 as the reducing agent is −7.02 eV, and with C2H4 is −3.45 eV. Transition state calculations show that O reduces the dissociation barrier of CH4 by about 2 eV. The smaller adsorption energy and negative reaction enthalpy of the product indicate that the iron-doped ZnAl2O4 has a good catalytic NO potential. This also provides a basis for future research on the catalytic mechanism of different hydrocarbons.

A plethora of information is now readily available for traffic prediction, making an effective use of them enables better traffic planning. With data coming from multiple sources, and their features spanning spatial and temporal dimensions, there is an increasing demand to exploit them for accurate traffic prediction. Existing methods, however, do not provide a solution for this, as they tend to require expertise feature engineering. In this paper, we propose a general architecture for S patio T emporal D ata F usion (STDF) with parameter efficiency. To make heterogeneous multi-source data fusion effectiveness, we separate all data into traffic directly related data and traffic indirectly related data. With traffic indirectly related data as the input to S patial E mbedding by T emporal convoluti ON (SETON) that simultaneously encodes each feature in both space and time dimensions and traffic directly related data as the input to the graph convolutional network(GCN), we designed a fine-grained feature transformer to match the ones generated by GCN. This is then followed by a fusion module to combine all features to make final prediction. Compared to using GCNs training with only traffic directly related data, experimental results show that our model can achieve a 6.1% improvement in prediction accuracy measured by Root Mean Squared Error.

With the development of the fifth-generation wireless network, autonomous moving platforms such as unmanned aerial vehicles (UAV) have been widely used in modern smart cities. In some applications, the UAVs need to perform certain monitoring tasks within a specified time. However, due to the energy constraints of UAVs, such tasks require using multiple UAVs to monitor multiple points. To solve this practical problem, this paper proposes a multi-UAV path planning model with the energy constraint (MUPPEC). The MUPPEC considers the energy consumption of a UAV in different states, such as acceleration, cruising speed, deceleration, and hovering, and the main objective of the MUPPEC is to minimize the total monitoring time. Also, a hybrid discrete intelligence algorithm based on the grey wolf optimizer (HDGWO) is proposed to solve the MUPPEC. In the HDGWO, the discrete grey wolf update operators are implemented, and the integer coding and greedy algorithms are used to transform between the grey wolf space and discrete problem space. Furthermore, the central position operation and stagnation compensation grey wolf update operation are introduced to improve the global convergence ability, and a two-opt with azimuth is designed to enhance the local search ability of the algorithm. Experimental results show that the HDGWO can solve the MUPPEC effectively, and compared to the traditional grey wolf optimizer(GWO), the discrete operators and the two-opt local search strategy with azimuth can effectively improve the optimization ability of the GWO.

Combing with photo-catalysis and photo-catalyst, a surface dielectric barrier discharge (SDBD) reactor with a mesh electrode was applied for toluene degradation and a high mineralization was achieved. The degradation performance comparison between SDBD reactors with a mesh and a spring electrode was carried out as well. A significant improvement in carbon balance and CO

With the exploding growth of smart devices and application of 5G communications, there are more and more computation-intensive tasks which are delay-sensitive. Mobile edge computing (MEC) is a new paradigm that provides rich computing resources in close proximity to mobile users. However, how to make the decision of whether offloading or not as well as control the transmission power jointly remains a challenge. In this paper, we first study the joint multiuser offloading and transmission power control optimization problem in a multi-channel wireless interference scenario. We formulate the joint optimization problem into a system-wide computation overhead minimization problem as a mixed integer non-linear program. We then propose an efficient semi-distributed algorithm consisting of two subalgorithms of offloading scheme and transmission power control. The numerical simulation results demonstrate that the proposed algorithm can effectively reduce system-wide computation overhead and increase the number of beneficial edge computing users, compared with all local or edge computing and fixed transmission power schemes.

A large number of public cameras are connected to the Internet, providing valuable information to modern society. We consider a cloud-based system that analyzes the streaming data from the cameras. It consists of a manager to dispatch jobs and workers to process the jobs. The workers are bound with various instances from public cloud that have different performance in terms of average frame rate and monetary cost. In this paper, we investigate the resource management on how to dispatch jobs to workers. First, we aim to maximize the minimum average frame rate of workers while satisfying the budget of renting computing instances from public cloud. Second, we aim to minimize the cost while satisfying the desired frame rate for stream analysis. For the former, we transform the maximization problem into a knapsack problem; for the latter, we transform the minimization problem as a bin packing problem. We develop efficient greedy algorithms to solve optimization problems with theoretical bounds. Simulation results indicate the tradeoff between performance and cost: as the budget of renting cloud instances, the benefit diminishes in achieving high average frame rate; as the number of cameras increases, more additional cost will incur to provide the desired frame rate.

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We prove that the space of intertwining operators associated with certain admissible modules over vertex operator algebras is isomorphic to a quotient of the vector space of conformal blocks on a three-pointed rational curve defined by the same data. This provides a new proof and alternative version of Frenkel and Zhu’s fusion rules theorem, in terms of the dimension of certain bimodules over Zhu’s algebra, without the assumption of rationality.

Combing with photo-catalysis and photo-catalyst, a surface dielectric barrier discharge (SDBD) reactor with a mesh electrode was applied for toluene degradation and a high mineralization was achieved. The degradation performance comparison between SDBD reactors with a mesh and a spring electrode was carried out as well. A significant improvement in carbon balance and CO2 selectivity were obtained in mesh SBDB reactor compared with that of spring's one. For instance, when only plasma was applied, the carbon balance and CO2 selectivity of mesh SDBD reactor were 84% and 42.6%, while only 64.5% and 31.8% in spring one, the carbon balance and CO2 selectivity were improved by 30.3% and 34% at SIE of 300 J L−1, respectively. Synergistic effects of photo-catalysis and photo-catalyst were conducted with a 254 nm UV lamp and TiO2 deposited on the mesh electrode by atmospheric pressure (AP) plasma technology. The results showed that TiO2 and UV irradiation both presented promoting effect on toluene degradation in SDBD reactor with mesh electrode. According to the experimental results, the carbon balance rose to 89.5% and 93.9% at SIE of 300 J L−1, when UV or TiO2 was applied. With the application of TiO2 and UV together, a highest carbon balance of 95.9% was obtained at the same SIE. At the same SIE, the CO2 selectivity was promoted by 42.8% or 55.3% with the application of UV or TiO2, and the promotion finally reached at 59.1% when TiO2 and UV were applied together. Additionally, the degradation efficiency of toluene was also enhanced with the introduction of TiO2 and UV irradiation. Increases in toluene degradation efficiency of 19. 7% and 26.8% were obtained at SIE of 300 J L−1, respectively. When both TiO2 and UV were applied, the enhancement could rise to 41.6%.

The user operates the smart home devices year in year out, have produced mass operation data, but these data do not be utilized well in past. Nowadays, these data can be used to predict user’s behavior custom with the development of big data and machine learning technologies, and then the prediction results can be employed to enhance the intelligence of a smart home system. In view of this, this paper proposes a novel unsupervised user behavior prediction (UUBP) algorithm, which employs an artificial neural network and proposes a forgetting factor to overcome the shortcomings of the previous prediction algorithm. This algorithm has a high-level of autonomous and self-organizing learning ability while does not require too much human intervention. Furthermore, the algorithm can better avoid the influence of user’s infrequent and out-of-date operation records, because of the forgetting factor. Finally, the use of real end user’s operation records to demonstrate that UUBP algorithm has a better level of performance than other algorithms from effectiveness.

Mn2O3/γ-Al2O3 catalysts was combined with double dielectric barrier discharge (DDBD) for degradation of acetone, toluene and ethyl acetate. Mn2O3/γ-Al2O3 catalysts with different Mn loading were synthesized by hydrothermal method. XRD, SEM, XPS and H2-TPR were applied to characterize the catalysts. Among the catalysts prepared, the Mn2O3/γ-Al2O3 catalysts with 5 wt% Mn loading presented the best performance in multicomponent VOCs degradation, which the highest removal efficiency (58.8% for acetone, 96.3% for toluene and 85.8% for ethyl acetate), the best carbon balance (87.5%) and CO2 selectivity (51.9%) were obtained at a specific input energy (SIE) of 700 J L−1. The formation of ozone was obviously inhibited with the introduction of Mn2O3/γ-Al2O3 catalysts. The higher Mn3+/Mn ratio, higher O2/O2− ratio and excellent low-temperature reducibility were beneficial for the VOCs degradation. Highly dispersed Mn2O3 crystals on the surface of γ-Al2O3 also might be an explanation for the improvement of VOCs degradation. According to the result of GC-MS, the variety of organic by-products gradually decreased with the increase of SIE, and the degradation mechanism of the mixed VOCs in plasma and on catalyst surface was discussed.

Toluene removal by double dielectric barrier charge (DDBD) plasma combined with a ZrMnFe/Sepiolite (SEP) catalyst was investigated and compared with the results from Fe/SEP, Mn/SEP and MnFe/SEP ones. All the catalysts were prepared by the impregnation method and characterized by XRD, BET, ICP, SEM, TEM, H2-TPR and XPS. The effect of catalysts on toluene degradation efficiency, carbon balance, CO2 selectivity and residual O3 concentration was studied. The experimental results indicated that the ZrMnFe/SEP catalyst presented the best catalytic performance. This is because of the high content of lattice oxygen contained in its surface, owing to the addition of Zr. When the SIE was 740 J/L, the highest toluene removal efficiency (87%), carbon balance (93%) and CO2 selectivity (51%) were obtained. The ZrMnFe/SEP catalyst had a better ozone inhibition effect than other catalysts. The catalyst has good stability, which the toluene removal efficiency, carbon balance and CO2 selectivity did not decrease significantly after 36 h of work at a constant energy density. The results indicated that the ZrMnFe/SEP catalyst is an efficient catalyst for degradation of toluene by plasma-catalyst measures.

Ag nanoparticles functionalized flower-like molybdenum disulfide (MoS2) hybrid nanostructures (AgNPs/MoS2) were successfully synthesized by a facile hydrothermal method. The structure and surface morphology were subsequently characterized by scanning electron microscopy (SEM), transmission electron microscopy (TEM), X-ray diffraction (XRD) and X-ray photoelectron spectroscopy (XPS) techniques. The as-synthesized AgNPs/MoS2 hybrid nanostructures were modified on a glassy carbon electrode (GCE) and further utilized for amperometric hydrogen peroxide (H2O2) detection. The electrochemical behaviors and sensing performance of the AgNPs/MoS2/GCE were studied by cyclic voltammetry (CV) and single-potential amperometry methods. The obtained results have demonstrated that the developed AgNPs/MoS2/GCE amperometric sensor possesses an excellent catalytic performance toward the reduction of H2O2. The as-prepared electrochemical sensor exhibits fast response time of less than 3 s, large linear detection range of 0.025-135.2 mM (R-2=0.998) and high sensitivity of 54.5 mu A.mM(-1). cm(-2). Moreover, the developed H2O2 sensor has shown good anti interference ability, outstanding stability and reproducibility, which represents a great potential for H2O2 detection in practical applications.

With the increasing number of vehicle and traffic jams, urban traffic management is becoming a serious issue. In this article, we propose novel four-tier architecture for urban traffic management with the convergence of VANETs, 5G networks, software-defined networks, and mobile edge computing technologies. The proposed architecture provides better communication and more rapid responsive speed in a more distributed and dynamic manner. The practical case of rapid accident rescue can significantly shorten the rescue time. Key technologies with respect to vehicle localization, data pre-fetching, traffic lights control, and traffic prediction are also discussed. Obviously, the novel architecture shows noteworthy potential for alleviating traffic congestion and improving the efficiency of urban traffic management.

Connected vehicles provide advanced transformations and attractive business opportunities in the automotive industry. Presently, IEEE 802.11p and evolving 5G are the mainstream radio access technologies in the vehicular industry, but neither of them can meet all requirements of vehicle communication. In order to provide low-latency and high-reliability communication, an SDN-enabled network architecture assisted by MEC, which integrates different types of access technologies, is proposed. MEC technology with its on-premises feature can decrease data transmission time and enhance quality of user experience in latency-sensitive applications. Therefore, MEC plays as important a role in the proposed architecture as SDN technology. The proposed architecture was validated by a practical use case, and the obtained results have shown that it meets application- specific requirements and maintains good scalability and responsiveness.

Text sentiment analysis is a fundamental task in the field of natural language processing (NLP). Recently, graph neural networks (GNNs) have achieved excellent performance in various NLP tasks. However, a GNN only considers the adjacent words when updating the node representations of the graph, and thus the model can only focus on the local features while ignoring global features. In this paper, we propose a novel multi-level graph neural network (MLGNN) for text sentiment analysis. To consider both local features and global features, we apply node connection windows with different sizes at different levels. Particularly, we integrate a scaled dot-product attention mechanism as a message passing mechanism into our method for fusing the features of each word node in the graph. The experimental results demonstrated that the proposed model outperformed other models in text sentiment analysis tasks.

Vehicular Cyber-Physical Systems are a rapidly growing research field that has applications in a variety of locations that include urban centers, underground parking lots, as well as tunnels environments. These systems require real-time or constant location information, but the traditional satellite signal-based positioning systems do not work well in tunnels or urban centers with dense skyscraper due to satellite signal Non-Line-Of-Sight (NLOS) problem. The One-Sided Synchronous Two-Way Ranging (OSS-TWR) algorithm can remedy the imprecise positioning of satellite signal-based positioning systems by using a Dedicated Short Range Communications (DSRC) link between the vehicle and Road Side Units (RSUs). However, the unavoidable network collisions in these wireless communication systems will severely influence the measurement of the signal flight of time, thus significantly degrading positioning estimation accuracy of OSS-TWR. In this paper, a cross-layer communication protocol is proposed to enhance OSS-TWR by compensating for the impact of network collision. Firstly, OSS-TWR positioning algorithm is analyzed and the reasons for the poor precision location estimation are further explored. Secondly, a time-recordable back-off algorithm is proposed that can record the total back-off time for a frame resend resulting from a network collision. Following this, the experimental ranging results show that the proposed algorithm is practically feasible and is superior to OSS-TWR. We proposed a new time-recordable back-off algorithm.A complete cross-layer communication protocol is proposed for vehicular positioning.We evaluate the proposed algorithm by ranging experiments.The proposed algorithm is practically feasible and is superior to OSS-TWR.

The present study aimed to assess the induction of epithelial-mesenchymal transition (EMT), invasion, and metastasis by the chemokine CXCL9/receptor CXCR3 axis in tongue squamous cell carcinoma (TSCC), unveiling the underlying mechanisms and providing new insights into the prevention and treatment of oral cancer metastasis. The expression levels of CXCL9 and CXCR3 in TSCC tissue specimens were determined by immunohistochemistry, assessing differences between samples with cervical lymph node metastasis and those without. Moreover, protein expression or activity in the TSCC Cal-27 cell line was controlled by neutralizing antibodies, gene transfection, or knock-out. Then, alterations of cell proliferation, migration, invasion, and the cytoskeleton were analyzed by CCK-8, cell scratch, Transwell, and cyto-skeleton staining assays, respectively. Alterations of EMT markers (E-cadherin and vimentin) in Cal-27 cells were detected by immunofluorescence and western blotting. In addition, western blotting was utilized to detect protein expression levels of Akt2, p-Akt2, eIF4E and p-eIF4E, and to explore the regulatory roles and mechanisms of the CXCL9/CXCR3 axis in invasion and metastasis. Significantly increased expression levels of CXCL9 and CXCR3 were detected in tissue specimens with lymph node metastasis compared with those without (P

Subsequently to the publication of this paper, an interested reader drew to the authors' attention that two pairs of data panels containing strikingly similar data were featured in Fig. 4A and B. The authors have re‑examined their data and realized that Fig. 4 was assembled incorrectly. The revised version of Fig. 4, containing the correct data for Fig. 4A and B, is shown on the next page. The authors regret the errors that were made in the preparation of the published figure, and confirm that these errors did not seriously affect the conclusions reported in the paper. The authors are grateful to the editor of Oncology Reports for allowing them the opportunity to publish a Corrigendum, and all the authors agree to this Corrigendum. Furthermore, they apologise to the readership for any inconvenience caused. [the original article was published in Oncology Reports 39: 1356-1368, 2018; DOI: 10.3892/or.2017.6169].

As for most location-aware applications need the precise location information, the wireless sensor networks (WSNs) are applied to positioning. But in some situation, Time Difference of Arrival (TDOA) positioning algorithm for WSNs may result in fuzzy positioning. This paper proposes a method to optimize fuzzy positioning of TODA through maximum likelihood estimation based on the identification of causes of fuzzy positioning resulted from TODA; the simu- lation experiment shows that TDOA positioning algorithm optimized by maximum likelihood estimation features high po- sitioning accuracy and excellent environmental adaptability, thus constituting a practicable solution

The Spt–Ada–Gcn5–acetyltransferase (SAGA) chromatin-modifying complex is a highly conserved 2-MDa protein complex comprised of ∼20 subunits (Helmlinger et al. 2004; Lee and Workman 2007; Weake and Workman 2012). The complex is arranged in a modular fashion and contains two enzymatic activities: an acetyltransferase activity associated with the GCN5/Pcaf subunit and a deubiquitinase activity associated with the yUbp8/dNon-stop/hUsp22 subunit (Lee et al. 2011). In yeast, the ataxin-7 homolog is Sgf73, and studies have shown that it anchors the deubiquitinase module (which includes Sgf73, Sgf11, Sus1, and Ubp8) to the SAGA complex (Lee et al. 2009, 2011; Weake and Workman 2012). Crystal structures and biochemical analysis of the yeast deubiquitinase module have shown that the N terminus of Sgf73 extends deep into the deubiquitinase module, intertwining between the components of the module to ensure an active conformation for the deubiquitinase. Without Sgf73, the deubiquitinase is inactive (Kohler et al. 2010; Samara et al. 2010). The carefully orchestrated addition and removal of ubiquitin on H2B are important regulators of transcription. H2B monoubiquitination is a prerequisite for di- and trimethylation of H3K4 and H3K79, modifications associated with transcriptionally active chromatin (Lee et al. 2007). Here, we identify the gene product of CG9866 as the Drosophila homolog of ataxin-7 (Ataxin-7). Biochemical analysis, including affinity purification, multidimensional protein identification technology (MudPIT) proteomic analysis, and gel filtration chromatography, establish that Ataxin-7 is a stable component of the SAGA chromatin remodeling complex. Analysis of SAGA from Ataxin-7-deficient flies revealed the loss of components from the SAGA complex, consistent with a role for Ataxin-7 in anchoring the deubiquitinase module to the complex. In contrast to the increased H2Bub observed upon loss of ataxin-7 in yeast, we observed a decrease in H2B ubiquitination in Drosophila with no associated changes in histone methylation and a reduction in the levels of H3K9 acetylation but not K3K14 acetylation. This surprising change in H2B ubiquitination was confirmed in human cells in which knockdown of human Ataxin-7 also resulted in decreased H2B ubiquitination. We hypothesize that this decrease reflects the release of an active deubiquitinase module from SAGA and, consequently, loss of SAGA-associated regulation of the deubiquitinase activity. Consistent with this model, we found that the deubiquitinase is enzymatically active when the complex is reconstituted in vitro even in the absence of Ataxin-7. An examination of flies with reduced expression of Ataxin-7 showed that loss of Ataxin-7 results in neural and retinal degeneration, impaired movement, and decreased life span.

This study chooses tourist attractions as the research object, and explores the response of female tourists to spokesmen of different tourist attractions. Inter-group experimental design was used to combine online network survey with off-line field survey. In data analysis, MATLAB software was used to analyze the variance of the experimental results, and to test the significance of the experimental results. In the aspect of result drawing, Origin software was used to render the result pictures. During the experiment, the subjects were randomly assigned to different experimental conditions to balance or reduce the errors caused by the differences between the subjects, so as to analyze the different responses of the subjects to stimuli. The results show that female tourists differ in their involvement of spokespersons for tourism products with different involvement degrees. On this basis, the brand strategy of tourist attractions is discussed and some suggestions are put forward.

In view of people's increasing demand for location-aware service, high-accuracy indoor localization has been considered the top priority of location-based service (LBS), therefore, the compact and cost-effective ZigBee technology with low power dissipation will undoubtedly be taken as one of the options for indoor localization within small area. As the accuracy cannot satisfy the application requirement, traditional localization ZigBee-based algorithm is abandoned gradually. This paper proposes a novel ZigBee-based indoor fingerprint localization algorithm and optimizes it through back propagation neural network (BPNN) interpolation method. Simulation result shows that this algorithm can significantly reduce the number of fingerprints and improve localization accuracy.

In order to overcome some problems, such as its low accuracy and failure in evaluating its performance, this paper use the weighted trilaminar data fusion of LS-RSSI to improve the incipient localization estimate values by analyze and study the lease square (LS) and Received Signal Strength Indication (RSSI) algorithm. As a result, we obtain a trilaminar data fusion localization algorithm of LS-RSSI, which has a better optimized localization estimate value. This algorithm has the advantages of limited numbers of calculation and is able to reduce the localization errors. As shown in the simulation, we are able to get a much more accuracy and stable localization estimate value with the trilaminar data fusion technology.

This paper proposes an eye control system employing eye gaze tracking techniques that might be helpful for those limb disabled people with healthy eyes. Eye gaze tracking technique is attracting more and more research interest in recent years. With an aim to overcome the shortcomings of existing methods (e.g., high hardware complexity and low detection accuracy), we use the pupil-corneal reflection technique to develop an ameliorated Hough transform algorithm, which is the core unit of the proposed system. We also design a typing function as well as an efficiently blink detection function. With these functions, users can input numbers into the computer with their eyes only through a specifically designed head mount eye control device. Experimental results demonstrate that the proposed ameliorated Hough transform algorithm provides a satisfactory typing accuracy of 87%, much higher than its counterpart in the literature.

SummaryThe formation of the larval body wall musculature of Drosophila depends on the asymmetric fusion of two myoblast types, founder cells (FCs) and fusion-competent myoblasts (FCMs). Recent studies have established an essential function of Arp2/3-based actin polymerization during myoblast fusion, formation of a dense actin focus at the site of fusion in FCMs, and a thin sheath of actin in FCs and/or growing muscles. The formation of these actin structures depends on recognition and adhesion of myoblasts that is mediated by cell surface receptors of the immunoglobulin superfamily. However, the connection of the cell surface receptors with Arp2/3-based actin polymerization is poorly understood. To date only the SH2-SH3 adaptor protein Crk has been suggested to link cell adhesion with Arp2/3-based actin polymerization in FCMs. Here, we propose that the SH2-SH3 adaptor protein Dock, like Crk, links cell adhesion with actin polymerization. We show that Dock is expressed in FCs and FCMs and colocalizes with the cell adhesion proteins Sns and Duf at cell–cell contact points. Biochemical data in this study indicate that different domains of Dock are involved in binding the cell adhesion molecules Duf, Rst, Sns and Hbs. We emphasize the importance of these interactions by quantifying the enhanced myoblast fusion defects in duf dock, sns dock and hbs dock double mutants. Additionally, we show that Dock interacts biochemically and genetically with Drosophila Scar, Vrp1 and WASp. Based on these data, we propose that Dock links cell adhesion in FCs and FCMs with either Scar– or Vrp1–WASp-dependent Arp2/3 activation.