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26 pages, 2285 KiB  
Article
Formal Security Reassessment of the 5G-AKA-FS Protocol: Methodological Corrections and Augmented Verification Techniques
by Yongho Ko, I Wayan Adi Juliawan Pawana and Ilsun You
Sensors 2024, 24(24), 7979; https://doi.org/10.3390/s24247979 - 13 Dec 2024
Viewed by 431
Abstract
The 5G-AKA protocol, a foundational component for 5G network authentication, has been found vulnerable to various security threats, including linkability attacks that compromise user privacy. To address these vulnerabilities, we previously proposed the 5G-AKA-Forward Secrecy (5G-AKA-FS) protocol, which introduces an ephemeral key pair [...] Read more.
The 5G-AKA protocol, a foundational component for 5G network authentication, has been found vulnerable to various security threats, including linkability attacks that compromise user privacy. To address these vulnerabilities, we previously proposed the 5G-AKA-Forward Secrecy (5G-AKA-FS) protocol, which introduces an ephemeral key pair within the home network (HN) to support forward secrecy and prevent linkability attacks. However, a re-evaluation uncovered minor errors in the initial BAN-logic verification and highlighted the need for more rigorous security validation using formal methods. In this paper, we correct the BAN-logic verification and advance the formal security analysis by applying an extended SVO logic, which was adopted as it provides a higher level of verification compared to BAN logic, incorporating a new axiom specifically for forward secrecy. Additionally, we enhance the ProVerif analysis by employing a stronger adversarial model. These refinements in formal verification validate the security and reliability of 5G-AKA-FS, ensuring its resilience against advanced attacks. Our findings offer a comprehensive reference for future security protocol verification in 5G networks Full article
(This article belongs to the Special Issue Feature Papers in the Internet of Things Section 2024)
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<p>The 5G-AKA-FS protocol.</p>
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<p>Generalized verification result of ProVerif of 5G-AKA-FS protocol.</p>
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<p>Robust verification result of ProVerif of 5G-AKA-FS protocol.</p>
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<p>SUCI replay attack process.</p>
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18 pages, 1317 KiB  
Article
ML-AKA: An Authentication Protocol for Non-Standalone 5G-Based C-IoT Networks
by Byomakesh Mahapatra, Vikash Singh, Rituraj Bhattacharjee and C. R. Srinivasan
Designs 2024, 8(6), 128; https://doi.org/10.3390/designs8060128 - 3 Dec 2024
Viewed by 636
Abstract
When it comes to the development of 4G and 5G technologies, long-range IoT or machine-to-machine (M2M) communication can be achieved with the help of cellular infrastructure. In non-standalone (NSA) 5G infrastructure, cellular-IoT (C-IoT) devices are attached and authenticated by a 4G core network [...] Read more.
When it comes to the development of 4G and 5G technologies, long-range IoT or machine-to-machine (M2M) communication can be achieved with the help of cellular infrastructure. In non-standalone (NSA) 5G infrastructure, cellular-IoT (C-IoT) devices are attached and authenticated by a 4G core network even if it is connected to a 5G base station. In an NSA-based 5G network, the presence of dual connectivity sometimes raises interoperability and authentication issues due to technological differences between LTE and 5G. An attacker explores these technological differences, introduces the threats, and performs various types of attacks like session hijacking at the interfaces and Man-in-the-Middle (MITM) attacks. With the introduction of these attacks, the attackers exploit the network resources and pinch out various critical information sources. To resolve this issue, the NSA-based C-IoT network must incorporate robust and seamless authentication and authorization mechanisms. This article presents the ML-AKA protocol that is used to enhance interoperability and trust between 4G and 5G networks by using a uniform key-sharing (UKS) mechanism. The proposed ML-AKA protocol is analyzed with the help of the AVISPA tool and validated with the use of Proverif. Further, the proposed protocol is compared with other existing protocols like EPS-AKA and UAKA-D2D, and the outcome shows that the proposed protocol significantly reduces the chances of MITM, DDOS and Spoofing attacks during the interoperability in the NSA-C-IoT network. Full article
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<p>End-to-end device communication over a NSA-5G-based C-IoT network.</p>
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<p>Key generation and authentication procedure in LTE network.</p>
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<p>EPS-AKA protocol of the NSA-based C-IoT network.</p>
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<p>A C-IoT communication framework in a personal area network.</p>
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<p>A C-IoT communication framework in a local area network.</p>
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<p>A C-IoT communication framework in global area network scenario.</p>
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<p>Authentication, registration, and acknowledgment flow diagram for ML-AKA protocol.</p>
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<p>Simulation result of ML-AKA protocol using AVISPA tools.</p>
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<p>Outcome of protocol variation with the help of ProVerif tool.</p>
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<p>Time cost of Phase IV operations.</p>
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<p>Communication overhead comparison for key agreement and session establishment using the ML-AKA protocol.</p>
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<p>Comparison of communication overhead for key agreement step.</p>
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<p>Comparison of communication overhead for session establishment step.</p>
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32 pages, 2926 KiB  
Article
Mitigating Security Vulnerabilities in 6G Networks: A Comprehensive Analysis of the DMRN Protocol Using SVO Logic and ProVerif
by Ilsun You, Jiyoon Kim, I Wayan Adi Juliawan Pawana and Yongho Ko
Appl. Sci. 2024, 14(21), 9726; https://doi.org/10.3390/app14219726 - 24 Oct 2024
Viewed by 1033
Abstract
The rapid evolution of mobile and optical communication technologies is driving the transition from 5G to 6G networks. This transition inevitably brings about changes in authentication scenarios, as new security demands emerge that go beyond the capabilities of existing frameworks. Therefore, it is [...] Read more.
The rapid evolution of mobile and optical communication technologies is driving the transition from 5G to 6G networks. This transition inevitably brings about changes in authentication scenarios, as new security demands emerge that go beyond the capabilities of existing frameworks. Therefore, it is necessary to address these evolving requirements and the associated key challenges: ensuring Perfect Forward Secrecy (PFS) to protect communications even if long-term keys are compromised and integrating Post-Quantum Cryptography (PQC) techniques to defend against the threats posed by quantum computing. These are essential for both radio and optical communications, which are foundational elements of future 6G infrastructures. The DMRN Protocol, introduced in 2022, represents a major advancement by offering both PFS and PQC while maintaining compatibility with existing 3rd Generation Partnership Project (3GPP) standards. Given the looming quantum-era challenges, it is imperative to analyze the protocol’s security architecture through formal verification. Accordingly, we formally analyze the DMRN Protocol using SVO logic and ProVerif to assess its effectiveness in mitigating attack vectors, such as malicious or compromised serving networks (SNs) and home network (HN) masquerading. Our research found that the DMRN Protocol has vulnerabilities in key areas such as mutual authentication and key exchange. In light of these findings, our study provides critical insights into the design of secure and quantum-safe authentication protocols for the transition to 6G networks. Furthermore, by identifying the vulnerabilities in and discussing countermeasures to address the DMRN Protocol, this study lays the groundwork for the future standardization of secure 6G Authentication and Key Agreement protocols. Full article
(This article belongs to the Special Issue Intelligent Optical Signal Processing in Optical Fiber Communication)
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<p>The 5G authentication protocols.</p>
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<p>DMRN Protocol.</p>
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<p>Formal verification categorization.</p>
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<p>Inference step of SVO logic.</p>
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<p>ProVerif structure.</p>
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<p>DMRN ProVerif architecture.</p>
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<p>DMRN flowchart diagram.</p>
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<p>(S1) Verification result of ProVerif of DMRN Protocol.</p>
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<p>(S2) Verification result of ProVerif.</p>
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<p>(S3) Verification result of ProVerif.</p>
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<p>(S4) Verification result of ProVerif of DMRN Protocol.</p>
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<p>(S1) attack process.</p>
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<p>(S2) attack process.</p>
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<p>(S3) attack process.</p>
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40 pages, 5898 KiB  
Article
Authentication and Key Agreement Protocol in Hybrid Edge–Fog–Cloud Computing Enhanced by 5G Networks
by Jiayi Zhang, Abdelkader Ouda and Raafat Abu-Rukba
Future Internet 2024, 16(6), 209; https://doi.org/10.3390/fi16060209 - 14 Jun 2024
Cited by 4 | Viewed by 1202
Abstract
The Internet of Things (IoT) has revolutionized connected devices, with applications in healthcare, data analytics, and smart cities. For time-sensitive applications, 5G wireless networks provide ultra-reliable low-latency communication (URLLC) and fog computing offloads IoT processing. Integrating 5G and fog computing can address cloud [...] Read more.
The Internet of Things (IoT) has revolutionized connected devices, with applications in healthcare, data analytics, and smart cities. For time-sensitive applications, 5G wireless networks provide ultra-reliable low-latency communication (URLLC) and fog computing offloads IoT processing. Integrating 5G and fog computing can address cloud computing’s deficiencies, but security challenges remain, especially in Authentication and Key Agreement aspects due to the distributed and dynamic nature of fog computing. This study presents an innovative mutual Authentication and Key Agreement protocol that is specifically tailored to meet the security needs of fog computing in the context of the edge–fog–cloud three-tier architecture, enhanced by the incorporation of the 5G network. This study improves security in the edge–fog–cloud context by introducing a stateless authentication mechanism and conducting a comparative analysis of the proposed protocol with well-known alternatives, such as TLS 1.3, 5G-AKA, and various handover protocols. The suggested approach has a total transmission cost of only 1280 bits in the authentication phase, which is approximately 30% lower than other protocols. In addition, the suggested handover protocol only involves two signaling expenses. The computational cost for handover authentication for the edge user is significantly low, measuring 0.243 ms, which is under 10% of the computing costs of other authentication protocols. Full article
(This article belongs to the Special Issue Key Enabling Technologies for Beyond 5G Networks)
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<p>Three-tier architecture.</p>
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<p>UML activity diagram for 3-Tier AKA: Entity registration protocol.</p>
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<p>UML activity diagram for 3-Tier AKA: Entity initialization protocol.</p>
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<p>3-Tier AKA: Entity registration protocol.</p>
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<p>UML activity diagram for 3-Tier AKA: Mutual authentication protocol.</p>
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<p>3-Tier AKA: Authentication and Key Agreement protocol for edge device and fog node.</p>
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<p>3-Tier AKA: Authentication and Key Agreement phase for two fog nodes.</p>
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<p>3-Tier AKA: Authentication and Key Agreement phase for fog node and cloud.</p>
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<p>3-Tier AKA: Handover authentication phase.</p>
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<p>5G-AKA protocol.</p>
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<p>4G EPS-AKA protocol.</p>
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<p>TLS 1.3 handshake.</p>
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<p>Signaling cost comparison.</p>
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<p>Communication cost comparison.</p>
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<p>Storage cost comparison of 3-Tier AKA, 5G-AKA [<a href="#B31-futureinternet-16-00209" class="html-bibr">31</a>], 4G EPS-AKA [<a href="#B31-futureinternet-16-00209" class="html-bibr">31</a>], and TLS 1.3 [<a href="#B58-futureinternet-16-00209" class="html-bibr">58</a>].</p>
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<p>FogHA handover authentication procedure.</p>
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<p>Quantum-resistant handover authentication protocol procedure.</p>
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<p>Liu et al.’s scheme authentication protocol procedure.</p>
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<p>Signaling cost comparison (handover) of 3-Tier AKA, FogHA [<a href="#B44-futureinternet-16-00209" class="html-bibr">44</a>], Zhang et al. [<a href="#B62-futureinternet-16-00209" class="html-bibr">62</a>], and Liu et al. [<a href="#B55-futureinternet-16-00209" class="html-bibr">55</a>].</p>
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<p>Communication cost comparison (handover) of 3-Tier AKA, FogHA [<a href="#B44-futureinternet-16-00209" class="html-bibr">44</a>], Zhang et al. [<a href="#B62-futureinternet-16-00209" class="html-bibr">62</a>], and Liu et al. [<a href="#B55-futureinternet-16-00209" class="html-bibr">55</a>].</p>
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<p>Storage cost comparison (handover) of 3-Tier AKA, FogHA [<a href="#B44-futureinternet-16-00209" class="html-bibr">44</a>], Zhang et al. [<a href="#B62-futureinternet-16-00209" class="html-bibr">62</a>], and Liu et al. [<a href="#B55-futureinternet-16-00209" class="html-bibr">55</a>].</p>
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18 pages, 1196 KiB  
Article
The Impact of Deficit Irrigation on the Agronomic Performance and Chemical Composition of Scolymus hispanicus L.
by Nikolaos Polyzos, Beatriz H. Paschoalinotto, Tânia C. S. P. Pires, Mikel Añibarro-Ortega, Ricardo Calhelha, Isabel C. F. R. Ferreira, Maria Inês Dias, Lillian Barros and Spyridon A. Petropoulos
Horticulturae 2024, 10(5), 479; https://doi.org/10.3390/horticulturae10050479 - 7 May 2024
Viewed by 1284
Abstract
In the current study, the effects of drought stress on the growth and phytochemical profile of Scolymus hispanicus L. (a.k.a. golden thistle) were evaluated. Plants were treated with three irrigation regimes, e.g., plants that received only rainwater (Control; C), deficit irrigation (I1; 50% [...] Read more.
In the current study, the effects of drought stress on the growth and phytochemical profile of Scolymus hispanicus L. (a.k.a. golden thistle) were evaluated. Plants were treated with three irrigation regimes, e.g., plants that received only rainwater (Control; C), deficit irrigation (I1; 50% of field capacity (FC)), and full irrigation (Ι2; 100% of FC). The fresh weight of the rosette of leaves was not negatively impacted by deficit irrigation, whereas root development was severely restrained compared to control and I2 treatments. Drought stress conditions had a positive effect on the nutritional properties of the golden thistle since the treatments of control and deficit irrigation showed the highest content of macronutrients and energy. Oxalic acid was the richest organic acid, especially under the I1 regime. Similarly, α-tocopherol was the only identified vitamin E isoform, whose content was also doubled in I1 treatment. Raffinose, glucose, and sucrose were the most abundant free sugars in amounts that varied among the irrigation treatments, while the total and distinct free sugar content was the highest for the I1 treatment. The most abundant detected fatty acid compounds were α-linolenic acid, followed by palmitic and linoleic acid, with the highest amount being detected in C, I1, and I2 treatments, respectively. Flavonoids were the only class of polyphenols detected in golden thistle leaves, including mostly kaempferol and quercetin derivatives. The greatest antioxidant potency was shown for the control and I1 treatments (for OxHLIA and TBARS methods, respectively). The evaluated leaf samples recorded a varied antimicrobial effect for the different bacterial strains and fungi, whereas no cytotoxic, hepatotoxic, and anti-inflammatory effects against the tested cell lines were recorded. Finally, the mineral content of leaves was significantly affected by the irrigation regime, with Ca, Mg, Cu, and Zn being the highest for the I1 treatment, while the I2 treatment had the highest content of K, Fe, and Mn and the lowest Na content. In conclusion, deficit irrigation showed promising results since it improved the phytochemical content without compromising the fresh weight of leaves, and thus it could be suggested as a sustainable agronomic practice for producing high-added value products without significant constraints in growth development and yield parameters of golden thistle. Full article
(This article belongs to the Special Issue Horticultural Production under Drought Stress)
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<p>Plants of <span class="html-italic">S. hispanicus</span> after crop establishment (left photo) and at full growth (right photo; photos are from the personal record of Spyridon A. Petropoulos).</p>
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23 pages, 1063 KiB  
Article
5G-AKA-FS: A 5G Authentication and Key Agreement Protocol for Forward Secrecy
by Ilsun You, Gunwoo Kim, Seonghan Shin, Hoseok Kwon, Jongkil Kim and Joonsang Baek
Sensors 2024, 24(1), 159; https://doi.org/10.3390/s24010159 - 27 Dec 2023
Cited by 4 | Viewed by 5475
Abstract
5G acts as a highway enabling innovative digital transformation and the Fourth Industrial Revolution in our lives. It is undeniable that the success of such a paradigm shift hinges on robust security measures. Foremost among these is primary authentication, the initial step in [...] Read more.
5G acts as a highway enabling innovative digital transformation and the Fourth Industrial Revolution in our lives. It is undeniable that the success of such a paradigm shift hinges on robust security measures. Foremost among these is primary authentication, the initial step in securing access to 5G network environments. For the 5G primary authentication, two protocols, namely 5G Authentication and Key Agreement (5G-AKA) and Improved Extensible Authentication Protocol Method for 3rd Generation Authentication and Key Agreement (EAP-AKA′), were proposed and standardized, where the former is for 3GPP devices, and the latter is for non-3GPP devices. Recent scrutiny has unveiled vulnerabilities in the 5G-AKA protocol, exposing it to security breaches, including linkability attacks. Moreover, mobile communication technologies are dramatically evolving while 3GPP has standardized Authentication and Key Management for Applications (AKMA) to reuse the credentials, generated during primary authentication, for 5G network applications. That makes it so significant for 5G-AKA to be improved to support forward secrecy as well as address security attacks. In response, several protocols have been proposed to mitigate these security challenges. In particular, they tried to strengthen security by reusing secret keys negotiated through the Elliptic Curve Integrated Encryption Scheme (ECIES) and countering linkability attacks. However, they still have encountered limitations in completing forward secrecy. Motivated by this, we propose an augmentation to 5G-AKA to achieve forward security and thwart linkability attacks (called 5G-AKA-FS). In 5G-AKA-FS, the home network (HN), instead of using its static ECIES key pair, generates a new ephemeral key pair to facilitate robust session key negotiation, truly realizing forward security. In order to thoroughly and precisely prove that 5G-AKA-FS is secure, formal security verification is performed by applying both BAN Logic and ProVerif. As a result, it is demonstrated that 5G-AKA-FS is valid. Besides, our performance comparison highlights that the communication and computation overheads are intrinsic to 5G-AKA-FS. This comprehensive analysis showcases how the protocol effectively balances between security and efficiency. Full article
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<p>3GPP 5G security architecture.</p>
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<p>The <tt>5G-AKA-FS</tt> protocol.</p>
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<p>Computations of <math display="inline"><semantics> <mrow> <mi>M</mi> <mi>A</mi> <mi>C</mi> <mo>,</mo> <mi>X</mi> <mi>R</mi> <mi>E</mi> <mi>S</mi> <mo>,</mo> <mi>C</mi> <mi>K</mi> <mo>,</mo> <mi>I</mi> <mi>K</mi> <mo>,</mo> <mi>A</mi> <mi>K</mi> </mrow> </semantics></math>, and <math display="inline"><semantics> <msub> <mi>K</mi> <mrow> <mi>A</mi> <mi>U</mi> <mi>S</mi> <mi>F</mi> </mrow> </msub> </semantics></math> in <tt>5G-AKA-FS</tt>.</p>
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<p>Types of formal verification.</p>
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<p>Total Computation Overhead.</p>
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13 pages, 2102 KiB  
Review
A Systematic Review of Lipid-Focused Cardiovascular Disease Research: Trends and Opportunities
by Uchenna Alex Anyaegbunam, Piyush More, Jean-Fred Fontaine, Vincent ten Cate, Katrin Bauer, Ute Distler, Elisa Araldi, Laura Bindila, Philipp Wild and Miguel A. Andrade-Navarro
Curr. Issues Mol. Biol. 2023, 45(12), 9904-9916; https://doi.org/10.3390/cimb45120618 - 9 Dec 2023
Cited by 2 | Viewed by 2851
Abstract
Lipids are important modifiers of protein function, particularly as parts of lipoproteins, which transport lipophilic substances and mediate cellular uptake of circulating lipids. As such, lipids are of particular interest as blood biological markers for cardiovascular disease (CVD) as well as for conditions [...] Read more.
Lipids are important modifiers of protein function, particularly as parts of lipoproteins, which transport lipophilic substances and mediate cellular uptake of circulating lipids. As such, lipids are of particular interest as blood biological markers for cardiovascular disease (CVD) as well as for conditions linked to CVD such as atherosclerosis, diabetes mellitus, obesity and dietary states. Notably, lipid research is particularly well developed in the context of CVD because of the relevance and multiple causes and risk factors of CVD. The advent of methods for high-throughput screening of biological molecules has recently resulted in the generation of lipidomic profiles that allow monitoring of lipid compositions in biological samples in an untargeted manner. These and other earlier advances in biomedical research have shaped the knowledge we have about lipids in CVD. To evaluate the knowledge acquired on the multiple biological functions of lipids in CVD and the trends in their research, we collected a dataset of references from the PubMed database of biomedical literature focused on plasma lipids and CVD in human and mouse. Using annotations from these records, we were able to categorize significant associations between lipids and particular types of research approaches, distinguish non-biological lipids used as markers, identify differential research between human and mouse models, and detect the increasingly mechanistic nature of the results in this field. Using known associations between lipids and proteins that metabolize or transport them, we constructed a comprehensive lipid–protein network, which we used to highlight proteins strongly connected to lipids found in the CVD-lipid literature. Our approach points to a series of proteins for which lipid-focused research would bring insights into CVD, including Prostaglandin G/H synthase 2 (PTGS2, a.k.a. COX2) and Acylglycerol kinase (AGK). In this review, we summarize our findings, putting them in a historical perspective of the evolution of lipid research in CVD. Full article
(This article belongs to the Special Issue A Focus on Molecular Basis in Cardiac Diseases)
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<p>Distribution of categories in papers associated with lipids and CVD. (<b>A</b>) Overlap between papers associated with human and mouse. (<b>B</b>) Overlap between papers associated with plasma, heart and myocardium. Only one paper was associated with plasma and heart, and another one with plasma and myocardium. (<b>C</b>) Overlap between papers associated with human and mouse for each of the three tissue/organ categories: plasma, heart, myocardium. Circles are not to scale for convenience of representation.</p>
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<p>Differences of frequency of MeSH CVD and lipid terms in new versus old publications. (<b>A</b>) For CVD. (<b>B</b>) For lipids. Old publications are from before 2010 and new publications are from the year 2010 and after. The diagonal represents the ratio of new versus old publications (356/1172 = 0.30).</p>
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<p>Evolution of usage of frequent MeSH CVD and lipid terms. (<b>A</b>) For CVD. (<b>B</b>) For lipids. The values represent numbers of articles using the term.</p>
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<p>Average number of MeSH CVD and lipid terms per paper versus time. CVD MeSH terms per paper (red) and Lipid MeSH terms per paper (blue) in four different time periods.</p>
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<p>Protein–lipid network and top two proteins selected. The network represents proteins (squares) connected to lipids (dots) that they metabolize or transport. Literature lipids (large squares; <a href="#app1-cimb-45-00618" class="html-app">Supplementary Table S6</a>) were used to score proteins connected to them: prostaglandin G/H synthase 2 (PTGS2; UniProt:P35354) and acylglycerol kinase (AGK; UniProt:Q53H12) were the two most connected proteins (large dots). See <a href="#cimb-45-00618-t002" class="html-table">Table 2</a> for other highly scored proteins (the full list is available as <a href="#app1-cimb-45-00618" class="html-app">Supplementary Table S7</a>). See text and Methods for details.</p>
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25 pages, 3633 KiB  
Article
BlobCUT: A Contrastive Learning Method to Support Small Blob Detection in Medical Imaging
by Teng Li, Yanzhe Xu, Teresa Wu, Jennifer R. Charlton, Kevin M. Bennett and Firas Al-Hindawi
Bioengineering 2023, 10(12), 1372; https://doi.org/10.3390/bioengineering10121372 - 29 Nov 2023
Cited by 4 | Viewed by 1775
Abstract
Medical imaging-based biomarkers derived from small objects (e.g., cell nuclei) play a crucial role in medical applications. However, detecting and segmenting small objects (a.k.a. blobs) remains a challenging task. In this research, we propose a novel 3D small blob detector called BlobCUT. BlobCUT [...] Read more.
Medical imaging-based biomarkers derived from small objects (e.g., cell nuclei) play a crucial role in medical applications. However, detecting and segmenting small objects (a.k.a. blobs) remains a challenging task. In this research, we propose a novel 3D small blob detector called BlobCUT. BlobCUT is an unpaired image-to-image (I2I) translation model that falls under the Contrastive Unpaired Translation paradigm. It employs a blob synthesis module to generate synthetic 3D blobs with corresponding masks. This is incorporated into the iterative model training as the ground truth. The I2I translation process is designed with two constraints: (1) a convexity consistency constraint that relies on Hessian analysis to preserve the geometric properties and (2) an intensity distribution consistency constraint based on Kullback-Leibler divergence to preserve the intensity distribution of blobs. BlobCUT learns the inherent noise distribution from the target noisy blob images and performs image translation from the noisy domain to the clean domain, effectively functioning as a denoising process to support blob identification. To validate the performance of BlobCUT, we evaluate it on a 3D simulated dataset of blobs and a 3D MRI dataset of mouse kidneys. We conduct a comparative analysis involving six state-of-the-art methods. Our findings reveal that BlobCUT exhibits superior performance and training efficiency, utilizing only 56.6% of the training time required by the state-of-the-art BlobDetGAN. This underscores the effectiveness of BlobCUT in accurately segmenting small blobs while achieving notable gains in training efficiency. Full article
(This article belongs to the Section Biosignal Processing)
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<p>Steps to identify blobs by using BlobCUT.</p>
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<p>The training process of our proposed BlobCUT.</p>
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<p>Illustration of blob identification through joint constraint operation. (<b>a</b>) Original noisy blobs image. (<b>b</b>) Clean blob image of blobs without noise. (<b>c</b>) Ground truth of blob centers. (<b>d</b>) Hessian convexity mask. (<b>e</b>) Blob mask from networks. (<b>f</b>) Final blob identification mask.</p>
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<p>Illustration of the training datasets used by different experiments. For Exp. I, synthetic noisy blob images were used as the source domain images; for Exp. II, real kidney MR images were used as the source domain images. For both experiments, synthetic clean blob images were used as target domain images to encourage the model to have denoising capability.</p>
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<p>Illustration of training input images of BlobCUT (<b>a</b>) Synthesized 3D blobs image from domain clean 3D blobs. (<b>b</b>) Blob mask of (<b>a</b>). (<b>c</b>) Synthesized 3D noisy blobs image from domain noisy 3D blobs. (<b>d</b>) Synthesized 3D mouse kidney image patch from domain noisy 3D blobs. (<b>e</b>) Real 3D mice kidney image patch from domain T.</p>
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<p>Illustration of the learning curve. (<b>a</b>) Loss curve comparison between training and validating of BlobCUT. (<b>b</b>) Testing F-score comparison between BlobCUT and BlobDetGAN.</p>
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<p>Comparison of glomerular segmentation results from 3D MR images of mouse kidneys using BlobCUT, BlobDetGAN, UH-DOG, BTCAS, UVCGAN and EGSDE. Identified glomeruli are marked in red. Three slices are illustrated: kidney #429 slice 111, #466 slice 96 and #469 slice 81.</p>
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<p>Denoising results of noisy synthetic blobs image using U-Net, 3D CUT, BlobCUT and compared with ground truth. (<b>a</b>) Original noisy blobs image. (<b>b</b>) Ground truth. (<b>c</b>) Denoised result of U-Net. (<b>d</b>) Denoised result of 3D CUT. (<b>e</b>) Denoised result of BlobCUT.</p>
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<p>Spherical coordinate systems.</p>
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10 pages, 1870 KiB  
Communication
Simultaneous Analysis of Hydrophobic Atractylenolides, Atractylon and Hydrophilic Sugars in Bai-Zhu Using a High-Performance Liquid Chromatography Column Tandem Technique
by Zhixing Gu, Xi Nie, Ping Guo, Yuehua Lu and Bo Chen
Foods 2023, 12(21), 3931; https://doi.org/10.3390/foods12213931 - 27 Oct 2023
Viewed by 1512
Abstract
An analytical method was established using high-performance liquid chromatography coupled with diode array and evaporative light scattering detectors (HPLC-DAD-ELSD) with -C18 and -NH2 column tandem for the simultaneous determination of hydrophobic atractylenolide I, II, III, atractylone and hydrophilic compounds glucose, fructose [...] Read more.
An analytical method was established using high-performance liquid chromatography coupled with diode array and evaporative light scattering detectors (HPLC-DAD-ELSD) with -C18 and -NH2 column tandem for the simultaneous determination of hydrophobic atractylenolide I, II, III, atractylone and hydrophilic compounds glucose, fructose and sucrose in the dried rhizome of Atractylodes macrocephala Koidz (a natural raw material for health foods, Bai-Zhu aka. in Chinese). The method combines the different separation capabilities of reversed-phase liquid chromatography and hydrophilic interaction liquid chromatography. It can provides a new choice for the simultaneous determination of hydrophilic and hydrophobic compounds in traditional Chinese medicines and health foods. It provided a reference method for the quality control of Bai-Zhu. The results showed that the linear correlation coefficients of the established column tandem chromatographic method were all greater than 0.9990, the relative standard deviation was 0.1–2.8%, and the average recovery was 96.7–103.1%. The contents of atractylenolide I, II, III, atractylone, fructose, glucose, and sucrose in 17 batches of Baizhu were 172.3–759.8 μg/g, 201.4–612.8 μg/g, 160.3–534.2 μg/g, 541.4–8723.1 μg/g, 6.9–89.7 mg/g, 0.7–7.9 mg/g, and 1.2–21.0 mg/g, respectively. Full article
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<p>Chemical structure of the compounds.</p>
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<p>UV absorption spectrum of (<b>a</b>): atractylenolide I; (<b>b</b>): atractylenolide II; (<b>c</b>): atractylenolide III; (<b>d</b>): atractylon.</p>
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<p>The chromatograms of atractylenolide I, II, III, atractylone and sugars on single column. (<b>upper</b>): -C<sub>18</sub>, 1: atractylenolide III, t<sub>R</sub>: 11.02 min.; 2: atractylenolide II, t<sub>R</sub>: 16.51 min.; 3: atractylenolide I, t<sub>R</sub>: 21.46 min.; 4: atractylone, t<sub>R</sub>: 36.78 min.; 5: sugars, t<sub>R</sub>: 2.13 min. (<b>lower</b>): -NH<sub>2</sub>, 1. terpenoids, t<sub>R</sub>: 2.51 min.; 2: fructose, t<sub>R</sub>: 8.32 min.; 3: glucose, t<sub>R</sub>: 9.45 min.; 4: sucrose, t<sub>R</sub>: 15.03 min). a: Mixed stanard solutions of atractylenolide I, II, III, atractylone and sugars; b: Bai-Zhu sample solution.</p>
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<p>The chromatograms of mixed standard solution (<b>a</b>) and Bai-Zhu sample solution (<b>b</b>) on -C<sub>18</sub> column in series with -NH<sub>2</sub> column (1: atractylenolide III, t<sub>R</sub>: 7.54 min.; 2: atractylenolide II, t<sub>R</sub>: 8.64 min.; 3: atractylenolide I, t<sub>R</sub>: 10.24 min.; 4: atractylone, t<sub>R</sub>: 28.12 min.; 5: glucose, t<sub>R</sub>: 15.26 min.; 6: fructose, t<sub>R</sub>: 16.37 min.; 7: sucrose, t<sub>R</sub>: 22.56 min.).</p>
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23 pages, 3213 KiB  
Article
On-Demand Anonymous Access and Roaming Authentication Protocols for 6G Satellite–Ground Integrated Networks
by Ya Tao, Haitao Du, Jie Xu, Li Su and Baojiang Cui
Sensors 2023, 23(11), 5075; https://doi.org/10.3390/s23115075 - 25 May 2023
Cited by 4 | Viewed by 1997
Abstract
Satellite–ground integrated networks (SGIN) are in line with 6th generation wireless network technology (6G) requirements. However, security and privacy issues are challenging with heterogeneous networks. Specifically, although 5G authentication and key agreement (AKA) protects terminal anonymity, privacy preserving authentication protocols are still important [...] Read more.
Satellite–ground integrated networks (SGIN) are in line with 6th generation wireless network technology (6G) requirements. However, security and privacy issues are challenging with heterogeneous networks. Specifically, although 5G authentication and key agreement (AKA) protects terminal anonymity, privacy preserving authentication protocols are still important in satellite networks. Meanwhile, 6G will have a large number of nodes with low energy consumption. The balance between security and performance needs to be investigated. Furthermore, 6G networks will likely belong to different operators. How to optimize the repeated authentication during roaming between different networks is also a key issue. To address these challenges, on-demand anonymous access and novel roaming authentication protocols are presented in this paper. Ordinary nodes implement unlinkable authentication by adopting a bilinear pairing-based short group signature algorithm. When low-energy nodes achieve fast authentication by utilizing the proposed lightweight batch authentication protocol, which can protect malicious nodes from DoS attacks. An efficient cross-domain roaming authentication protocol, which allows terminals to quickly connect to different operator networks, is designed to reduce the authentication delay. The security of our scheme is verified through formal and informal security analysis. Finally, the performance analysis results show that our scheme is feasible. Full article
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<p>SGIN overall architecture.</p>
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<p>SGIN protocol model.</p>
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<p>Authentication phase in the unlinkable authentication scenario.</p>
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<p>Authentication phase in the batch authentication scenario.</p>
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<p>Pre-negotiation and roaming authentication phase in SGIN.</p>
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<p>Results from the implementations of the unlinkable authentication scenario using ProVerif.</p>
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<p>Results from the implementations of the batch authentication scenario using ProVerif.</p>
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<p>Computational cost comparison of batch authentication in the verification phase. Xue et al. [<a href="#B11-sensors-23-05075" class="html-bibr">11</a>], BGS [<a href="#B12-sensors-23-05075" class="html-bibr">12</a>].</p>
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<p>Case comparison for rebatch authentication.</p>
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<p>Communication overhead comparison in the unlinkable authentication scenario. Feng et al. [<a href="#B37-sensors-23-05075" class="html-bibr">37</a>], Alamer [<a href="#B24-sensors-23-05075" class="html-bibr">24</a>], Wasef et al. [<a href="#B12-sensors-23-05075" class="html-bibr">12</a>].</p>
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19 pages, 5103 KiB  
Article
Development of pH-Responsive N-benzyl-N-O-succinyl Chitosan Micelles Loaded with a Curcumin Analog (Cyqualone) for Treatment of Colon Cancer
by Sasikarn Sripetthong, Fredrick Nwude Eze, Warayuth Sajomsang and Chitchamai Ovatlarnporn
Molecules 2023, 28(6), 2693; https://doi.org/10.3390/molecules28062693 - 16 Mar 2023
Cited by 9 | Viewed by 2264
Abstract
This work aimed at preparing nanomicelles from N-benzyl-N,O-succinyl chitosan (NBSCh) loaded with a curcumin analog, 2,6-bis((3-methoxy-4-hydroxyphenyl) methylene) cyclohexanone, a.k.a. cyqualone (CL), for antineoplastic colon cancer chemotherapy. The CL-loaded NBSCh micelles were spherical and less than 100 nm in [...] Read more.
This work aimed at preparing nanomicelles from N-benzyl-N,O-succinyl chitosan (NBSCh) loaded with a curcumin analog, 2,6-bis((3-methoxy-4-hydroxyphenyl) methylene) cyclohexanone, a.k.a. cyqualone (CL), for antineoplastic colon cancer chemotherapy. The CL-loaded NBSCh micelles were spherical and less than 100 nm in size. The entrapment efficiency of CL in the micelles ranged from 13 to 39%. Drug release from pristine CL was less than 20% in PBS at pH 7.4, whereas the release from CL-NBSCh micelles was significantly higher. The release study of CL-NBSCh revealed that around 40% of CL content was released in simulated gastric fluid at pH 1.2; 79 and 85% in simulated intestinal fluids at pH 5.5 and 6.8, respectively; and 75% in simulated colonic fluid at pH 7.4. CL-NBSCh showed considerably high selective cytotoxicity towards mucosal epithelial human colon cancer (HT-29) cells and lower levels of toxicity towards mouse connective tissue fibroblasts (L929). CL-NBSCh was also more cytotoxic than the free CL. Furthermore, compared to free CL, CL-NBSCh micelles were found to be more efficient at arresting cell growth at the G2/M phase, and induced apoptosis earlier in HT-29 cells. Collectively, these results indicate the high prospective potential of CL-loaded NBSCh micelles as an oral therapeutic intervention for colon cancer. Full article
(This article belongs to the Special Issue Advances on Nanomedicine and Nanoparticle-Based Drug Delivery)
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<p>Structure of cyqualone (CL, 2,6-bis((3-methoxy-4-hydroxyphenyl) methylene) cyclohexanone).</p>
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<p>Pictures of blank NBSCh micellar solution (<b>a</b>) and NBSCh micellar solutions containing CL at different concentrations (<b>b</b>) 0.1, (<b>c</b>) 0.3, (<b>d</b>) 0.6, (<b>e</b>) 1.0 mg/mL.</p>
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<p>TEM micrographs of blank NBSCh and CL-loaded-NBSCh micelles.</p>
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<p>Dissolution profiles at 37 °C during 24 h of CL in simulated gastric fluid (SGF pH = 1.2, <span style="color:#843C0B">●</span>), simulated extracellular tumor cells fluid (ETC, pH 5.5, <span style="color:#002060">●</span>), simulated intestinal fluids (SIF, pH = 6.8, <span style="color:#538135">●</span>) and simulated colon fluid (SCF, pH = 7.4, <span style="color:#308DC0">●</span>) and CL from CL-loaded NBSCh micelles in SGF pH 1.2 (<span style="color:#FFC000">●</span>), ETC, pH 5.5 (<span style="color:#A6A6A6">●</span>), pH 6.8 (<span style="color:#0D60B3">●</span>) and SCF, pH = 7.4 (<span style="color:#ED7D31">●</span>).</p>
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<p>Gastrointestinal-simulated cumulative release profiles of CL (<span style="color:#44546A">●</span>) and CL from CL-loaded NBSCh micelles (<span style="color:#ED7D31">●</span>) in SGF pH 1.2 (0–2 h), followed by SIF pH 6.8 (2–8 h) and finally in SCF pH 7.4 (8–24 h).</p>
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<p>TEM micrographs (<b>a</b>), particle sizes (<b>b</b>) and zeta potentials (<b>c</b>) of 1 mg/mL blank NBSCh micelle in different pH mediums.</p>
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<p>Sizes of CL-loaded NBSCh micelles following redispersion of powders in distilled water (<b>a</b>). Zeta-potential values of the CL-loaded NBSCh micelles freeze-dried powders during storage (<b>b</b>) stored at 4 °C (<span style="color:#2F5496">●</span>) and 30 °C (<span style="color:#C00000">●</span>) for 120 days.</p>
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<p>HT-29 (<b>a</b>) and L929 (<b>b</b>) cells’ viability (%) of the CL (<span style="color:#4472C4">■</span>), CL micelle (<span style="color:#ED7D31">■</span>) and blank micelle (<span style="color:#7B7B7B">■</span>) by MTT assay.</p>
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<p>CLSM analysis of NBSCh micelles uptake by human colon cancer cells (HT-29) 6 h and 24 h after exposure. Blue fluorescence represents the cell nuclei stained by Hoechst 33,342 stain. Green fluorescence represents the micelles labelled by FITC.</p>
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<p>The effect of curcumin analog (CL) and CL-loaded NBSCh micelles on induced apoptosis of HT-29 human colon cancer cells. Lower left quadrant represents viable cells that do not bind to AnnexinV-FitC or PI; lower right quadrant represent early apoptotic cells binding to only AnnexinV-FitC; top right quadrant represent necrotic or late apoptotic cells that were both AnnexinV-FitC- and PI-positive; and top left quadrant represent necrotic cells binding to only PI.</p>
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<p>Cell cycle distribution of HT-29 human colon cancer cells exposed for 24 h to culture medium (control) (<span style="color:#5B9BD5">■</span>), blank NBSCh micelles (<span style="color:#ED7D31">■</span>), free curcumin analog (<span style="color:#A6A6A6">■</span>) and CL-loaded NBSCh micelles (<span style="color:#FFC000">■</span>). * &lt; 0.05 (<span class="html-italic">n</span> = 3).</p>
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<p>Synthesis scheme for <span class="html-italic">N</span>-benzyl-<span class="html-italic">N</span>,<span class="html-italic">O</span>-succinyl chitosan (NBSCh).</p>
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20 pages, 5165 KiB  
Article
Precision Detection and Assessment of Ash Death and Decline Caused by the Emerald Ash Borer Using Drones and Deep Learning
by Sruthi Keerthi Valicharla, Xin Li, Jennifer Greenleaf, Richard Turcotte, Christopher Hayes and Yong-Lak Park
Plants 2023, 12(4), 798; https://doi.org/10.3390/plants12040798 - 10 Feb 2023
Cited by 11 | Viewed by 2515
Abstract
Emerald ash borer (Agrilus planipennis) is an invasive pest that has killed millions of ash trees (Fraxinus spp.) in the USA since its first detection in 2002. Although the current methods for trapping emerald ash borers (e.g., sticky traps and [...] Read more.
Emerald ash borer (Agrilus planipennis) is an invasive pest that has killed millions of ash trees (Fraxinus spp.) in the USA since its first detection in 2002. Although the current methods for trapping emerald ash borers (e.g., sticky traps and trap trees) and visual ground and aerial surveys are generally effective, they are inefficient for precisely locating and assessing the declining and dead ash trees in large or hard-to-access areas. This study was conducted to develop and evaluate a new tool for safe, efficient, and precise detection and assessment of ash decline and death caused by emerald ash borer by using aerial surveys with unmanned aerial systems (a.k.a., drones) and a deep learning model. Aerial surveys with drones were conducted to obtain 6174 aerial images including ash decline in the deciduous forests in West Virginia and Pennsylvania, USA. The ash trees in each image were manually annotated for training and validating deep learning models. The models were evaluated using the object recognition metrics: mean average precisions (mAP) and two average precisions (AP50 and AP75). Our comprehensive analyses with instance segmentation models showed that Mask2former was the most effective model for detecting declining and dead ash trees with 0.789, 0.617, and 0.542 for AP50, AP75, and mAP, respectively, on the validation dataset. A follow-up in-situ field study conducted in nine locations with various levels of ash decline and death demonstrated that deep learning along with aerial survey using drones could be an innovative tool for rapid, safe, and efficient detection and assessment of ash decline and death in large or hard-to-access areas. Full article
(This article belongs to the Collection Application of AI in Plants)
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<p>Example aerial view of declining or dead ash trees from aerial images obtained with drones (<b>a</b>), ground view of declining or dead ash trees (<b>b</b>), and a noticeable opposite branching pattern of ash trees on the aerial images (<b>c</b>). The ground survey confirmed the presence of emerald ash borers with adults’ exit holes (<b>d</b>). Yellow arrows in (<b>a</b>,<b>b</b>) indicate declining and dead ash trees, and arrows in (<b>c</b>,<b>d</b>) indicate an opposite branching pattern and D-shaped exit holes made by adult emerald ash borers, respectively.</p>
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<p>Precision–Recall (P–R) curves for ash trees showing different sizes (large, medium, and small) of ash canopy dieback. C75, IoU = 0.75; C50, IoU = 0.5; Loc, Localization; Sim, Similar objects; Oth, Other; BG, background; FN, false negative. The sizes of the canopy dieback were categorized based on pixel size: small (area &lt; 32<sup>2</sup> pixels), medium (32<sup>2</sup> pixels &lt; area &lt; 96<sup>2</sup> pixels), and large (area &gt; 96<sup>2</sup> pixels).</p>
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<p>Two examples of Mask2former’s predictions of declining or dead ash trees with a threshold of 0.75. Input aerial images were given to the model, and then the prediction was compared with the ground-truth data.</p>
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<p>Example in-situ validation for the detection of declining or dead ash trees using Mask2former, a supervised deep learning model with two different confidence levels: 0.1 (<b>a</b>) and 0.5 (<b>b</b>). White boxes indicate declining or dead ash trees located by ground survey, and purple areas (bounding box) indicate declining or dead ash trees detected and located by Mask2former. By comparing ground survey data with Mask2former prediction map, type I and II errors could be calculated.</p>
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<p>Prediction of Mask2former model on images taken in autumn; (<b>a</b>) is an input image, and (<b>b</b>,<b>c</b>) are the predictions with a threshold of 0.5 and 0.1, respectively.</p>
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<p>Locations of study sites (blue and red dots) in Pennsylvania and West Virginia, USA. The red dot indicates the site where data for deep learning were collected, and the blue dots indicate in-situ validation study sites.</p>
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<p>An example of the original RGB image (<b>a</b>) and its manual annotations (<b>b</b>) of declining and dead ash trees, which are outlined in red.</p>
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<p>An illustration of patches generated from a 4K image acquired with drones. A total of 18 patches with dimensions of 640 pixels by 720 pixels were extracted from each 4K image for deep learning. Image patches (<b>a</b>,<b>b</b>) without declining ash trees, and image patch (<b>c</b>) has a declining or dead ash tree.</p>
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<p>Workflow for binary segmentation mask generation. An RGB image (<b>a</b>) is converted into grayscale information of the RGB image (<b>b</b>) and red channel information of the RGB image (<b>c</b>). Then, a subtracted image (<b>d</b>) is used to convert to a binary image (<b>e</b>), and the final mask is generated after applying the morphological filter (<b>f</b>).</p>
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<p>The overall workflow for the prediction of ash tree decline and death.</p>
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<p>The overall network architecture of the deep learning model (Mask2former). The input RGB image is fed to the backbone to extract low-resolution features. The pixel decoder block upsamples the low-resolution features. The decoder block takes in the image features and queries and provides the segmentation mask and class label. The final output of the model is an RGB image with a bounding box, class label, and a segmentation mask on the predicted dead or declining ash trees.</p>
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<p>Drone operation and image analysis protocol used in this study.</p>
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22 pages, 4142 KiB  
Article
Novel Aeromonas Phage Ahy-Yong1 and Its Protective Effects against Aeromonas hydrophila in Brocade Carp (Cyprinus aka Koi)
by Lingting Pan, Dengfeng Li, Wei Lin, Wencai Liu, Chenxin Qu, Minhua Qian, Ruqian Cai, Qin Zhou, Fei Wang and Yigang Tong
Viruses 2022, 14(11), 2498; https://doi.org/10.3390/v14112498 - 11 Nov 2022
Cited by 9 | Viewed by 2317
Abstract
Aeromonas hydrophila is a zoonotic pathogen and an important fish pathogen. A new lytic phage, Ahy-yong1, against multi-antibiotic-resistant pathogen A. hydrophila was isolated, identified, and tentatively used in therapy. Ahy-yong1 possesses a head of approximately 66 nm in diameter and a short tail [...] Read more.
Aeromonas hydrophila is a zoonotic pathogen and an important fish pathogen. A new lytic phage, Ahy-yong1, against multi-antibiotic-resistant pathogen A. hydrophila was isolated, identified, and tentatively used in therapy. Ahy-yong1 possesses a head of approximately 66 nm in diameter and a short tail of approximately 26 nm in length and 32 nm in width. Its complete dsDNA genome is 43,374 bp with a G + C content of 59.4%, containing 52 predicted opening reading frames (ORFs). Taxonomic analysis indicated Ahy-yong1 as a new species of the Ahphunavirus genus of the Autographiviridae family of the Caudoviricetes class. Ahy-yong1 was active only against its indicator host strain among the 35 strains tested. It is stable at 30–40 °C and at pH 2–12. Aeromonas phage Ahy-yong1 revealed an effective biofilm removal capacity and an obvious protective effect in brocade carp (Cyprinus aka Koi). The average cumulative mortality for the brocade carp in the blank groups intraperitoneally injected with PBS was 1.7% ± 2.4%;for the control groups treated with A. hydrophila (108 CFU/fish) via intraperitoneal injection, it was 100.00%;and for the test group I, successively treated with A. hydrophila (108 CFU/fish) and Aeromonas phage Ahy-yong1 (107 PFU/fish) via intraperitoneal injection witha time interval of 2 hours, it was only 43.4% ± 4.7%. Furthermore, the cumulative mortality of the test group II, successively treated with Aeromonas phage Ahy-yong1 (107 PFU/fish) and A. hydrophila (108 CFU/fish), was only 20.0% ± 8.2%, and that of the test group III, simultaneously treated with Aeromonas phage Ahy-yong1 (107 PFU/fish) and A. hydrophila (108 CFU/fish), was only 30.0% ± 8.2%. The results demonstrated that phage Ahy-yong1 was very effective in the therapies against A. hydrophila A18, prophylaxis was more effective than rescue, and earlier treatment was better for the reduction of mortality. This study enriches knowledge about Aeromonas phages. Full article
(This article belongs to the Section Bacterial Viruses)
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<p>Visualization of Ahy-yong1 plaques and <span class="html-italic">Aeromonas hydrophila</span> A18 cultures. (<b>A</b>) <span class="html-italic">Aeromonas</span> phage Ahy-yong1 formed clear and circular plaques on <span class="html-italic">A. hydrophila</span> A18 lawns. Small plaques can be seen in 3 h, and the average diameter of the plaques reached 0.84 mm in 4 h. (<b>B</b>) Normal <span class="html-italic">A. hydrophila</span> A18 culture (left) and lysateofphageAhy-yong1-infected <span class="html-italic">A. hydrophila</span> A18 (right).</p>
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<p>Transmission electron microscopy of negatively stained <span class="html-italic">Aeromonas</span> phage Ahy-yong1 and <span class="html-italic">A. hydrophila</span> A18 cell infected with Ahy-yong1. (<b>A</b>–<b>C</b>) Free intact-matureAhy-yong1 virion. Ahy-yong1 possesses a head with a diameter of 66 nm and a short non-contractile tail of 26 nm in length and 32 nm in width under a transmission electron microscope. (<b>D</b>) Immature phage particles being packaged within a viral factory in an infected <span class="html-italic">A. hydrophila</span> A18 cell.</p>
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<p>The one-step growth curve under the MOI of 0.1 and the results of the temperature stability and pH stability test of <span class="html-italic">Aeromonas</span> phage Ahy-yong1. (<b>A</b>) The one-step growth curve demonstrated that the latent period of <span class="html-italic">Aeromonas</span> phage Ahy-yong1 was 10 min, followed by a burst period of 50 min. (<b>B</b>) <span class="html-italic">Aeromonas</span> phage Ahy-yong1 was very stable at 30 °C, maintaining constant production for over 120 min; relatively stable at 40 °C; not stable at 50 °C, 60 °C, and 70 °C. Phage Ahy-yong1 eventually became inactive when the temperature exceeded 50 °C. (<b>C</b>) <span class="html-italic">Aeromonas</span> phage Ahy-yong1 was found to be stable at pH 2 to 12, indicating that even extremes of pH could not affect infectivity of phage Ahy-yong1. All values represent the mean of triplicate measurements, and error bars represent the standard deviations (<span class="html-italic">n</span> = 3).</p>
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<p>The ability of <span class="html-italic">A. hydrophila</span> A18 to form biofilms and the ability of <span class="html-italic">Aeromonas</span> phage Ahy-yong1 to eliminate biofilm. (<b>A</b>,<b>B</b>) The OD<sub>590</sub> of values of the crystal-violet-stained biofilm and cells. (<b>C</b>,<b>D</b>) The wells stained with crystal violet. All values represent the mean of triplicate measurements, and error bars represent the standard deviations (<span class="html-italic">n</span> = 3). ** <span class="html-italic">p</span> &lt; 0.01 and *** <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>Genomic map of <span class="html-italic">Aeromonas</span> phage Ahy-yong1. From outside to inside, circle 1 shows the 52 predicted ORFs, circle 2 shows the size in pairs (kb), circle 3 displays the GC of the genome, and the innermost circle shows the GC skew plot (G − C)/(G + C). The direction of the arrow indicates the transcription direction of each gene. The color of each gene refers to the most similar phages, yellow stands for the gene similar to the <span class="html-italic">Aeromonas</span> phage and gray for the gene without similarity in the NCBI database.</p>
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<p>Genome comparison of the <span class="html-italic">Aeromonas</span> phage Ahy-yong1 and the three closest relatives (<span class="html-italic">Aeromonas</span> phage LAh1, <span class="html-italic">Aeromonas</span> phage CF7, and <span class="html-italic">Aeromonas</span> phage Ahp1). The orientation of the arrows indicates the direction of gene transcription. The color of each arrow refers to the functional categories: blue indicates DNA replication and regulation; yellow indicates DNA packaging; red indicates lysis; orange indicates structure; purple indicates RNA polymerase; gray indicates hypothetical protein. The homologous regions are represented by green bars. Light green to dark green represent low to high homology between genes.</p>
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<p>Proteomic tree based on the complete genome sequences of <span class="html-italic">Aeromonas</span> phage Ahy-yong1, 62 classified phages of <span class="html-italic">Caudoviricetes</span> class with shorter evolutionary distance from Ahy-yong1 in the original tree and unclassified <span class="html-italic">Aeromonas</span> phage LAh1 sharing the top highest homology with Ahy-yong1 in BLASTn scanning. Bacteriophage family assignments according to the official ICTV classification (March 2022) are provided with different color bars. The red star indicates phage Ahy-yong1. In the proteomic tree, <span class="html-italic">Aeromonas</span> phage Ahy-yong1 clustered with <span class="html-italic">Aeromonas</span> phages of the family <span class="html-italic">Autographiviridae</span>, especially closely related with <span class="html-italic">Aeromonas</span> phage LAh1, Ahp1, and CF7.</p>
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<p>The internal organs and cumulative mortality curves of brocade carps (<span class="html-italic">Cyprinus aka</span> Koi) in the blank group, control group, and test groups. Each fish in the blank group was successively injected intraperitoneally twice with 0.01 M PBS. Each fish in the control group was successively injected intraperitoneally with <span class="html-italic">A. hydrophila</span> A18 (10<sup>8</sup> CFU/mL) and 0.01 M PBS. Each fish in the test group I was successively injected intraperitoneally with <span class="html-italic">A. hydrophila</span> A18 (10<sup>8</sup> CFU/mL) and <span class="html-italic">Aeromonas</span> phage Ahy-yong1 (10<sup>7</sup> PFU/mL). Each fish in the test group II was successively injected intraperitoneally with <span class="html-italic">Aeromonas</span> phage Ahy-yong1 (10<sup>7</sup> PFU/mL) and <span class="html-italic">A. hydrophila</span> A18 (10<sup>8</sup> CFU/mL). The injection time intervals were 2 h and the injection volume was 100 µL. Each fish in the test group III was injected with 100 µL of <span class="html-italic">Aeromonas</span> phage Ahy-yong1 (10<sup>7</sup> PFU/mL) immediately after the injection of 100 µL of <span class="html-italic">A. hydrophila</span> A18 (10<sup>8</sup> CFU/mL). (<b>A</b>) The internal organs of brocade carps infected with <span class="html-italic">A. hydrophila</span> (control groups) were swollen and rotten and their eyes become cloudy relative to the blank groups and test groups. (<b>B</b>) The cumulative mortality of brocade carps in the blank groups was 1.7% ± 2.4%. The cumulative mortality of brocade carps in the test groups I, II, and III were 43.3% ± 4.7%, 20.0% ± 8.2%, and 30.0% ± 8.2%, respectively, which were significantly lower than those in the control groups, of which the cumulative mortality was 100.0%. All values represent the mean of triplicate measurements, and error bars represent the standard deviations (<span class="html-italic">n</span> = 3).</p>
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<p>The dynamic curves of phage load and <span class="html-italic">A. hydrophila</span> A18 load in the muscles of the brocade carps. (<b>A</b>) The dynamic curves of phage load in the muscles of the brocade carps. (<b>B</b>) The dynamic curves of <span class="html-italic">A. hydrophila</span> A18 load in the muscles of the brocade carps. All values represent the mean of triplicate measurements, and error bars represent the standard deviations (<span class="html-italic">n</span> = 3).</p>
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7 pages, 1118 KiB  
Proceeding Paper
Arduino-Based Sensing Platform for Rapid, Low-Cost, and High-Sensitivity Detection and Quantification of Analytes in Fluidic Samples
by Derek Hayden, Sergio Anacleto, Daphne-Eleni Archonta, Nour Khalil, Antonia Pennella, Shadan Qureshi, Alexandre Séguin and Nima Tabatabaei
Eng. Proc. 2022, 27(1), 69; https://doi.org/10.3390/ecsa-9-13277 - 1 Nov 2022
Viewed by 1645
Abstract
Lateral flow assays (LFAs; aka. rapid tests) are inexpensive paper-based devices for rapid and specific detection of analyte of interest (e.g., COVID virus) in fluidic samples. Areas of application of LFAs cover a broad spectrum, spanning from agriculture to food/water safety to point-of-care [...] Read more.
Lateral flow assays (LFAs; aka. rapid tests) are inexpensive paper-based devices for rapid and specific detection of analyte of interest (e.g., COVID virus) in fluidic samples. Areas of application of LFAs cover a broad spectrum, spanning from agriculture to food/water safety to point-of-care medical testing and, most recently, to detection of COVID-19 infection. While these low-cost and rapid tests are specific to the target analyte, their sensitivity and limit of detection are far inferior to their laboratory-based counterparts. In addition, rapid tests normally cannot quantify the concentration of target analyte and only provide qualitative/binary detection. We have developed a low-cost, end-user sensing platform that significantly improves the sensitivity of rapid tests. The developed platform is based on Arduino and utilizes low-cost far infrared, single-element detectors to offer sensitive and semi-quantitative results from commercially available rapid tests. The sensing paradigm integrated to the low-cost device is based on radiometric detection of photothermal responses of rapid tests in the frequency domain when exposed to modulated laser excitation. As a proof of principle, we studied commercially available rapid tests for detection of THC (the principal psychoactive constituent of cannabis) in oral fluid with different concentrations of control positive solutions and, subsequently, interpret them with the developed sensor. Results suggest that the developed end-user sensor is not only able to improve the detection limit of the rapid test by approximately an order of magnitude from 25 ng/mL to 5 ng/mL, but also offers the ability to obtain semi-quantitative insight into concentration of THC in the fluidic samples. Full article
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<p>A competitive-style LFA where the presence or absence of target analyte either blocks or allows binding of antibodies to the antigens in the test line.</p>
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<p>(<bold>a</bold>) Rendering of the device, (<bold>b</bold>) electronic system overview featuring the laser and motor control systems and sensor sampling scheme.</p>
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<p>(<bold>a</bold>) Interpolated and resamples time series of control and test line responses, (<bold>b</bold>) control and test line responses transformed to the frequency domain.</p>
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<p>Results of 10 repetitions per LFA (N = 20 per concentration): (<bold>a</bold>) control line versus normalized test line response, (<bold>b</bold>) normalized responses fit to a quadratic curve, *, **, ***, **** indicate the number of standard deviations between pairs of means.</p>
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22 pages, 4087 KiB  
Article
Probiotic as Adjuvant Significantly Improves Protection of the Lanzhou Trivalent Rotavirus Vaccine against Heterologous Challenge in a Gnotobiotic Pig Model of Human Rotavirus Infection and Disease
by Viviana Parreno, Muqun Bai, Fangning Liu, Jiqiang Jing, Erika Olney, Guohua Li, Ke Wen, Xingdong Yang, Tammy Bui Castellucc, Jacob F. Kocher, Xu Zhou and Lijuan Yuan
Vaccines 2022, 10(9), 1529; https://doi.org/10.3390/vaccines10091529 - 14 Sep 2022
Cited by 9 | Viewed by 2531
Abstract
This preclinical study in the gnotobiotic (Gn) pig model of human rotavirus (HRV) infection and disease evaluates the effect of probiotic Lactobacillus rhamnosus GG (LGG) as a mucosal adjuvant on the immunogenicity and cross-protective efficacy of the Lanzhou live oral trivalent (G2, G3, [...] Read more.
This preclinical study in the gnotobiotic (Gn) pig model of human rotavirus (HRV) infection and disease evaluates the effect of probiotic Lactobacillus rhamnosus GG (LGG) as a mucosal adjuvant on the immunogenicity and cross-protective efficacy of the Lanzhou live oral trivalent (G2, G3, G4) vaccine (TLV, aka LLR3). Gn pigs were immunized with three doses of TLV with or without concurrent administration of nine doses of LGG around the time of the first dose of the TLV vaccination, and were challenged orally with the virulent heterotypic Wa G1P[8] HRV. Three doses of TLV were highly immunogenic and conferred partial protection against the heterotypic HRV infection. LGG significantly enhanced the intestinal and systemic immune responses and improved the effectiveness of protection against the heterotypic HRV challenge-induced diarrhea and virus shedding. In conclusion, we demonstrated the immune-stimulating effects of probiotic LGG as a vaccine adjuvant and generated detailed knowledge regarding the cross-reactive and type-specific antibody and effector B and T cell immune responses induced by the TLV. Due to the low cost, ease of distribution and administration, and favorable safety profiles, LGG as an adjuvant has the potential to play a critical role in improving rotavirus vaccine efficacy and making the vaccines more cost-effective. Full article
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<p>Experimental design: Gnotobiotic (Gn) pig immunization, <span class="html-italic">Lactobacillus rhamnosus</span> (LGG) feeding, challenge, and sampling.</p>
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<p>Representative images of fecal virus shedding were detected by VP7-specific RT-PCR. The shedding of reassortant virus strains in TLV from the Gn pigs in the TLV+LGG group was monitored after the oral administration of each vaccine dose. (<b>a</b>) Fecal swabs collected after dose 1 inoculation; (<b>b</b>) fecal swabs collected after dose 2 inoculation; (<b>c</b>) fecal swabs collected after dose 3 inoculation; (<b>d</b>) RT-PCR amplification of G2, G3, and G4 DNA fragments from mixed positive control by using rotavirus VP7-specific G2, G3, and G4 typing primers detailed in <a href="#vaccines-10-01529-t001" class="html-table">Table 1</a>.</p>
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<p>Protection against diarrhea upon virulent Wa HRV challenge. Pigs were challenged orally with virulent Wa HRV at PID 28 and monitored for 7 days post-challenge (PCDs 1–7) for duration and severity of diarrhea. * Indicates significant difference when comparing between two groups; ** indicates significant difference between the TLV+LGG group and all the other groups. (<b>a</b>) Mean diarrhea score from PCD 0 to 7 in all treatment groups. (<b>b</b>) Mean diarrhea score of TLV+LGG versus the control. (<b>c</b>) Mean days to onset of diarrhea. (<b>d</b>) Mean duration of diarrhea. (<b>e</b>) Mean area under the curve of diarrhea score.</p>
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<p>Protection against virus shedding upon virulent Wa HRV challenge. Pigs were challenged orally with virulent Wa HRV at PID 28 and monitored 7 days postchallenge (PCDs 1−7) for duration and magnitude of virus shedding by CCIF. * Indicates significant difference when compared with the LGG or control group. (<b>a</b>) Mean virus shedding from PCDs 1 to 7 compared among all groups or compared pairwise between different groups. (<b>b</b>) Mean duration of virus shedding measured by cell culture immunofluorescence (CCIF). (<b>c</b>) Mean area under the curve (AUC) of virus shedding in each treatment group.</p>
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<p>IgA and IgG VP7 G-type-specific ASC responses in the ileum at challenge (PID 28/PCD 0) in (<b>a</b>) pigs orally immunized with TLV vaccine, and (<b>b</b>) pigs immunized with TLV vaccine in the presence of LGG. The ASC responses to different VP7 G-types were statistically similar within each vaccine group (Kruskal–Wallis non-parametric rank sum test, <span class="html-italic">p</span> &lt; 0.05). Mean in the same column with different capital letters indicates significant differences between the TLV and TLV+LGG groups for the same G type. Lines and error bars represent the mean and SEM.</p>
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<p>IgA and IgG VP7–specific ASC responses in the intestinal and systemic lymphoid tissues prechallenge (PID 28/PCD 0) (a and b) and postchallenge (PCD 7) (c and d). <sup>#</sup> Indicates a significant difference between PID 28/PCD 0 and PCD 7 for each vaccine group in each tissue for each VP7 G–type * Indicates a significant difference between TLV+LGG and TLV groups for the same VP7 type in the same tissue and timepoint (Kruskal–Wallis non–parametric rank sum test, <span class="html-italic">p</span> &lt; 0.05). Pigs in the LGG and mock control groups did not develop VP7–specific ASC responses (<a href="#app1-vaccines-10-01529" class="html-app">Table S1</a>).</p>
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<p>VP7 G-type specific IgA (<b>left</b> panel) and IgG (<b>right</b> panel) antibody responses detected by immunocytochemical staining assay in serum of Gn pigs vaccinated with TLV with or without LGG. Bars with different uppercase letters (A, B) indicate a statistical difference between treatment groups (repeated measure ANOVA-GLM, <span class="html-italic">p</span> &lt; 0.05). Bars with different lowercase letters (a, b) indicate significant differences among VP7 G-type specific antibody responses within each vaccine in the selected timepoint (one-way ANOVA, <span class="html-italic">p</span> &lt; 0.05). Blue arrows indicate the immunization times (3 ×) and grey arrows the challenge time with virulent Wa HRV G1P[8] strain.</p>
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<p>Virus neutralizing antibody responses to Wa HRV pre- and postchallenge in TLV and TLV+LGG groups. Bars with different uppercase letters (A, B) differs significantly. Antibody titers between the two groups on each timepoint were not significantly different. VN antibody titers of both groups increased significantly postchallenge (repeated measure ANOVA-GLM, <span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Frequencies of IFN-γ+CD4+ and IFN-γ+CD8+ T cells in the intestinal and systemic lymphoid tissues detected by flow cytometry pre-challenge (PD 28/PCD 0) in the LGG-fed, TLV-vaccinated, and mock control pigs. The MNC were stimulated in vitro for 17 h with semi-purified whole Wa HRV antigen or mock stimulated. Capital letters on top of bars (A, B, C) indicate significant difference, whereas shared letters indicate no difference among the groups for the same cell type (Kruskal–Wallis test, <span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Frequencies of IFN-γ+CD4+ and IFN-γ+CD8+ T cells in the intestinal and systemic lymphoid tissues pre- and postchallenge in the TLV vaccinated Gn pigs detected by flow cytometry. The MNC were stimulated in vitro for 17 h with semi-purified whole Wa HRV, G2, G3, or G4 VP7 antigens. * Indicates a significant difference compared between PID 28 and PCD 7 for the same cell type. Different letters on the bars indicate significant differences between different antigen stimulation for IFN-γ+CD4 T cells (capital letters A, B) and IFN-γ+CD8 T cells (lowercase letters a, b); while shared letters indicate no significant difference at the same time point (Kruskal–Wallis test, <span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Frequencies of IFN-γ+CD4+ and IFN-γ+CD8+ T cells in the intestinal and systemic lymphoid tissues of Gn pigs in the TLV+LGG, TLV, and LGG groups at PCD 7 detected by flow cytometry. The MNC were stimulated in vitro for 17 h with semi-purified whole Wa HRV, G2, G3 and G4 VP7 antigens, or mock stimulated. Bars with different uppercase letters (A, B) indicate significant differences among groups for the same cell type, while shared letters indicate no difference (Kruskal–Wallis test, <span class="html-italic">p</span> &lt; 0.05).</p>
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