52 research outputs found
Deletion of the meq gene significantly decreases immunosuppression in chickens caused by pathogenic marek's disease virus
<p>Abstract</p> <p>Background</p> <p>Marek's disease virus (MDV) causes an acute lymphoproliferative disease in chickens, resulting in immunosuppression, which is considered to be an integral aspect of the pathogenesis of Marek's disease (MD). A recent study showed that deletion of the Meq gene resulted in loss of transformation of T-cells in chickens and a Meq-null virus, rMd5ΔMeq, could provide protection superior to CVI988/Rispens.</p> <p>Results</p> <p>In the present study, to investigate whether the Meq-null virus could be a safe vaccine candidate, we constructed a Meq deletion strain, GX0101ΔMeq, by deleting both copies of the Meq gene from a pathogenic MDV, GX0101 strain, which was isolated in China. Pathogenesis experiments showed that the GX0101ΔMeq virus was fully attenuated in specific pathogen-free chickens because none of the infected chickens developed Marek's disease-associated lymphomas. The study also evaluated the effects of GX0101ΔMeq on the immune system in chickens after infection with GX0101ΔMeq virus. Immune system variables, including relative lymphoid organ weight, blood lymphocytes and antibody production following vaccination against AIV and NDV were used to assess the immune status of chickens. Experimental infection with GX0101ΔMeq showed that deletion of the Meq gene significantly decreased immunosuppression in chickens caused by pathogenic MDV.</p> <p>Conclusion</p> <p>These findings suggested that the Meq gene played an important role not only in tumor formation but also in inducing immunosuppressive effects in MDV-infected chickens.</p
Seeing is Believing: Detecting Sybil Attack in FANET by Matching Visual and Auditory Domains
The flying ad hoc network (FANET) will play a crucial role in the B5G/6G era
since it provides wide coverage and on-demand deployment services in a
distributed manner. The detection of Sybil attacks is essential to ensure
trusted communication in FANET. Nevertheless, the conventional methods only
utilize the untrusted information that UAV nodes passively ``heard'' from the
``auditory" domain (AD), resulting in severe communication disruptions and even
collision accidents. In this paper, we present a novel VA-matching solution
that matches the neighbors observed from both the AD and the ``visual'' domain
(VD), which is the first solution that enables UAVs to accurately correlate
what they ``see'' from VD and ``hear'' from AD to detect the Sybil attacks.
Relative entropy is utilized to describe the similarity of observed
characteristics from dual domains. The dynamic weight algorithm is proposed to
distinguish neighbors according to the characteristics' popularity. The
matching model of neighbors observed from AD and VD is established and solved
by the vampire bat optimizer. Experiment results show that the proposed
VA-matching solution removes the unreliability of individual characteristics
and single domains. It significantly outperforms the conventional RSSI-based
method in detecting Sybil attacks. Furthermore, it has strong robustness and
achieves high precision and recall rates.Comment: 7 pages, 9 figures, 1 tabl
Dual Identities Enabled Low-Latency Visual Networking for UAV Emergency Communication
The Unmanned Aerial Vehicle (UAV) swarm networks will play a crucial role in
the B5G/6G network thanks to its appealing features, such as wide coverage and
on-demand deployment. Emergency communication (EC) is essential to promptly
inform UAVs of potential danger to avoid accidents, whereas the conventional
communication-only feedback-based methods, which separate the digital and
physical identities (DPI), bring intolerable latency and disturb the unintended
receivers. In this paper, we present a novel DPI-Mapping solution to match the
identities (IDs) of UAVs from dual domains for visual networking, which is the
first solution that enables UAVs to communicate promptly with what they see
without the tedious exchange of beacons. The IDs are distinguished dynamically
by defining feature similarity, and the asymmetric IDs from different domains
are matched via the proposed bio-inspired matching algorithm. We also consider
Kalman filtering to combine the IDs and predict the states for accurate
mapping. Experiment results show that the DPI-Mapping reduces individual
inaccuracy of features and significantly outperforms the conventional
broadcast-based and feedback-based methods in EC latency. Furthermore, it also
reduces the disturbing messages without sacrificing the hit rate.Comment: 6 pages, 6 figure
Specific Beamforming for Multi-UAV Networks: A Dual Identity-based ISAC Approach
Beam alignment is essential to compensate for the high path loss in the
millimeter-wave (mmWave) Unmanned Aerial Vehicle (UAV) network. The integrated
sensing and communication (ISAC) technology has been envisioned as a promising
solution to enable efficient beam alignment in the dynamic UAV network.
However, since the digital identity (D-ID) is not contained in the reflected
echoes, the conventional ISAC solution has to either periodically feed back the
D-ID to distinguish beams for multi-UAVs or suffer the beam errors induced by
the separation of D-ID and physical identity (P-ID). This paper presents a
novel dual identity association (DIA)-based ISAC approach, the first solution
that enables specific, fast, and accurate beamforming towards multiple UAVs. In
particular, the P-IDs extracted from echo signals are distinguished dynamically
by calculating the feature similarity according to their prevalence, and thus
the DIA is accurately achieved. We also present the extended Kalman filtering
scheme to track and predict P-IDs, and the specific beam is thereby effectively
aligned toward the intended UAVs in dynamic networks. Numerical results show
that the proposed DIA-based ISAC solution significantly outperforms the
conventional methods in association accuracy and communication performance.Comment: 7 pages, 8 figure
Deletion of 1.8-kb mRNA of Marek's disease virus decreases its replication ability but not oncogenicity
<p>Abstract</p> <p>Background</p> <p>The 1.8-kb mRNA was reported as one of the oncogenesis-related genes of Marek's disease virus (MDV). In this study, the bacterial artificial chromosome (BAC) clone of a MDV field strain GX0101 was used as the platform to generate mutant MDV to examine the functional roles of 1.8-kb mRNA.</p> <p>Results</p> <p>Based on the BAC clone of GX0101, the 1.8-kb mRNA deletion mutant GX0101Δ(A+C) was constructed. The present experiments indicated that GX0101Δ(A+C) retained a low level of oncogenicity, and it showed a decreased replication capacity in vitro and in vivo when compared with its parent virus, GX0101. Further studies in vitro demonstrated that deletion of 1.8-kb mRNA significantly decreased the transcriptional activity of the bi-directional promoter between 1.8-kb mRNA and pp38 genes of MDV.</p> <p>Conclusion</p> <p>These results suggested that the 1.8-kb mRNA did not directly influence the oncogenesis but related to the replication ability of MDV.</p
Metabolomics in the Development and Progression of Dementia: A Systematic Review
Dementia has become a major global public health challenge with a heavy economic burden. It is urgently necessary to understand dementia pathogenesis and to identify biomarkers predicting risk of dementia in the preclinical stage for prevention, monitoring, and treatment. Metabolomics provides a novel approach for the identification of biomarkers of dementia. This systematic review aimed to examine and summarize recent retrospective cohort human studies assessing circulating metabolite markers, detected using high-throughput metabolomics, in the context of disease progression to dementia, including incident mild cognitive impairment, all-cause dementia, and cognitive decline. We systematically searched the PubMed, Embase, and Cochrane databases for retrospective cohort human studies assessing associations between blood (plasma or serum) metabolomics profile and cognitive decline and risk of dementia from inception through October 15, 2018. We identified 16 studies reporting circulating metabolites and risk of dementia, and six regarding cognitive performance change. Concentrations of several blood metabolites, including lipids (higher phosphatidylcholines, sphingomyelins, and lysophophatidylcholine, and lower docosahexaenoic acid and high-density lipoprotein subfractions), amino acids (lower branched-chain amino acids, creatinine, and taurine, and higher glutamate, glutamine, and anthranilic acid), and steroids were associated with cognitive decline and the incidence or progression of dementia. Circulating metabolites appear to be associated with the risk of dementia. Metabolomics could be a promising tool in dementia biomarker discovery. However, standardization and consensus guidelines for study design and analytical techniques require future development
UD-MAC: Delay Tolerant Multiple Access Control Protocol for Unmanned Aerial Vehicle Networks
In unmanned aerial vehicle (UAV) networks, high-capacity data transmission is
of utmost importance for applications such as intelligent transportation, smart
cities, and forest monitoring, which rely on the mobility of UAVs to collect
and transmit large amount of data, including video and image data. Due to the
short flight time of UAVs, the network capacity will be reduced when they
return to the ground unit for charging. Hence, we suggest that UAVs can apply a
store-carry-and-forward (SCF) transmission mode to carry packets on their way
back to the ground unit for improving network throughput. In this paper, we
propose a novel protocol, named UAV delay-tolerant multiple access control
(UD-MAC), which can support different transmission modes in UAV networks. We
set a higher priority for SCF transmission and analyze the probability of being
in SCF mode to derive network throughput. The simulation results show that the
network throughput of UD-MAC is improved by 57% to 83% compared to VeMAC
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