Recognition of HIV-1 capsid by PQBP1 licenses an innate immune sensing of nascent HIV-1 DNA.

We have previously described polyglutamine-binding protein 1 (PQBP1) as an adapter required for the cyclic GMP-AMP synthase (cGAS)-mediated innate response to the human immunodeficiency virus 1 (HIV-1) and other lentiviruses. Cytoplasmic HIV-1 DNA is a transient and low-abundance pathogen-associated molecular pattern (PAMP), and the mechanism for its detection and verification is not fully understood. Here, we show a two-factor authentication strategy by the innate surveillance machinery to selectively respond to the low concentration of HIV-1 DNA, while distinguishing these species from extranuclear DNA molecules. We find that, upon HIV-1 infection, PQBP1 decorates the intact viral capsid, and this serves as a primary verification step for the viral nucleic acid cargo. As reverse transcription and capsid disassembly initiate, cGAS is recruited to the capsid in a PQBP1-dependent manner. This positions cGAS at the site of PAMP generation and sanctions its response to a low-abundance DNA PAMP.

NASTRA: accurate analysis of short tandem repeat markers by nanopore sequencing with repeat-structure-aware algorithm.

Short-tandem repeats (STRs) are the type of genetic markers extensively utilized in biomedical and forensic applications. Due to sequencing noise in nanopore sequencing, accurate analysis methods are lacking. We developed NASTRA, an innovative tool for Nanopore Autosomal Short Tandem Repeat Analysis, which overcomes traditional database-based methods' limitations and provides a precise germline analysis of STR genetic markers without the need for allele sequence reference. Demonstrating high accuracy in cell line authentication testing and paternity testing, NASTRA significantly surpasses existing methods in both speed and accuracy. This advancement makes it a promising solution for rapid cell line authentication and kinship testing, highlighting the potential of nanopore sequencing for in-field applications.

Determination of adulteration, geographical origins, and species of food by mass spectrometry.

Food adulteration, mislabeling, and fraud, are rising global issues. Therefore, a number of precise and reliable analytical instruments and approaches have been proposed to ensure the authenticity and accurate labeling of food and food products by confirming that the constituents of foodstuffs are of the kind and quality claimed by the seller and manufacturer. Traditional techniques (e.g., genomics-based methods) are still in use; however, emerging approaches like mass spectrometry (MS)-based technologies are being actively developed to supplement or supersede current methods for authentication of a variety of food commodities and products. This review provides a critical assessment of recent advances in food authentication, including MS-based metabolomics, proteomics and other approaches.

Mass spectrometry in food authentication and origin traceability.

Food authentication and origin traceability are popular research topics, especially as concerns about food quality continue to increase. Mass spectrometry (MS) plays an indispensable role in food authentication and origin traceability. In this review, the applications of MS in food authentication and origin traceability by analyzing the main components and chemical fingerprints or profiles are summarized. In addition, the characteristic markers for food authentication are also reviewed, and the advantages and disadvantages of MS-based techniques for food authentication, as well as the current trends and challenges, are discussed. The fingerprinting and profiling methods, in combination with multivariate statistical analysis, are more suitable for the authentication of high-value foods, while characteristic marker-based methods are more suitable for adulteration detection. Several new techniques have been introduced to the field, such as proton transfer reaction mass spectrometry, ambient ionization mass spectrometry (AIMS), and ion mobility mass spectrometry, for the determination of food adulteration due to their fast and convenient analysis. As an important trend, the miniaturization of MS offers advantages, such as small and portable instrumentation and fast and nondestructive analysis. Moreover, many applications in food authentication are using AIMS, which can help food authentication in food inspection/field analysis. This review provides a reference and guide for food authentication and traceability based on MS.

Experimental study on the detection of Gastrodia elata by enzymatic recombinase amplification and immunochromatography.

OBJECTIVE: The objective of this research is to develop two methodologies, Enzymatic recombinase amplification (ERA) and Polymerase Chain Reaction (PCR) coupled with Lateral Flow Dipstick (LFD), for the swift authentication of Gastrodia elata. METHODOLOGY: Primers and nfo probes for the ERA of Gastrodia elata were developed based on the ITS2 genome sequences of Gastrodia elata and its counterfeits. Specific primers for the PCR analysis of Gastrodia elata were generated using the NCBI (National Center for Biotechnology Information) online platform. Through experimental validation, the optimal reaction system and conditions for both methodologies were established, and their efficacy was assessed. RESULTS: The methodologies developed herein are applicable for the targeted analysis of the medicinal species, Gastrodia elata. The sensitivity of the ERA-LFD detection method matched that of the conventional PCR-LFD approach, recorded at 1 ng µL-1. Consistency was observed in the results across three replicates of visualization test strips for both techniques. Upon evaluation, both the PCR-LFD and ERA-LFD methods demonstrated a total compliance rate of 100 %. CONCLUSION: The ERA-LFD and PCR-LFD methods facilitate reduced detection times and offer visual results. These techniques are particularly effective for on-site detection and quality control in the authentication of Gastrodia elata within traditional Chinese medicine markets and at the primary level of healthcare provision.

Comparison of competitive and sandwich immunochromatographic analysis in the authentication of chicken in meat products.

Cheap chicken meat is often used as an undeclared substitute in meat products. In this study, two formats of the immunochromatographic assay (ICA) of immunoglobulins of class Y (IgY) as a biomarker for chicken authentication were developed. In both competitive ICA (cICA) and sandwich ICA (sICA), gold nanoparticles (GNP) were conjugated with anti-species antibodies. A simple procedure of sample preparation, which took only 30 min, was proposed. Test systems demonstrated high sensitivity and rapidity: visual limits of detection of IgY and assay durations were 12/14 ng/mL and 10/15 min for cICA and sICA, respectively. The absence of cross-reactivity with the mammalian species confirmed the high specificity of the test systems. Good applicability of the assays was confirmed for the detection of chicken in raw meat mixtures: as low as 3% and 0.2% (w/w) of chicken could be revealed in beef and pork by cICA and sICA, respectively. The influence of heat processing of meat-based products on immune recognition and, consequently, the analytical performance of the test systems was revealed. It was shown that sICA is preferable for the detection of IgY even in thermally processed meat. The proposed ICAs can be recommended for rapid on-site control of meat products' composition.

Identification of adulteration in the market samples of saffron using morphology, HPLC, HPTLC, and DNA barcoding methods.

Saffron, the stigma of Crocus sativus L., is the most expensive spice used for culinary, medicinal, dye, and cosmetics purposes. It is highly adulterated because of its limited production and high commercial value. In this study, 104 saffron market samples collected from 16 countries were tested using morphology, high-performance liquid chromatography (HPLC), high-performance thin-layer chromatography (HPTLC), and deoxyribonucleic acid (DNA) barcoding. Overall, 45 samples (43%) were adulterated. DNA barcoding identified the highest number of adulterated saffron (44 samples), followed by HPTLC (39 samples), HPLC (38 samples), and morphology (32 samples). Only DNA barcoding identified the adulterated samples containing saffron and other plants' parts as bulking agents. In addition, DNA barcoding identified 20 adulterant plant species, which will help develop quality control methods and market surveillance. Some of the adulterant plants are unsafe for human consumption. The HPLC method helped identify the saffron samples adulterated with synthetic safranal. HPLC and HPTLC methods will help identify the samples adulterated with other parts of the saffron plant (auto-adulteration).

Existing status and future advancements of adulteration detection techniques in herbal products.

BACKGROUND: Herbal products have been commonly used all over the world for centuries. Its products have gained remarkable acceptance as therapeutic agents for a variety of disorders. However, following recent research disclosing discrepancies between labeling and actual components of herbal products, there is growing concern about the efficacy, quality and safety of the products. The admixture and adulteration of herbal medicinal products pose a risk of serious health compromise and the well-being of the consumers. To prevent adulteration in raw ingredients and final herbal products, it is necessary to use approaches to assess both genomes as well as metabolomics of the products; this offers quality assurance in terms of product identification and purity. The combinations of molecular and analytical methods are inevitable for thorough verification and quality control of herbal medicine. METHODS AND RESULTS: This review discusses the combination of DNA barcoding, DNA metabarcoding, mass spectroscopy as well as HPLC for the authentication of herbal medicine and determination of the level of adulteration. It also discusses the roles of PCR and real-time PCR techniques in validating and ensuring the quality, purity and identity of the herbal products. CONCLUSIONS: In conclusion, each technique has its own pros and cons, but the cumulative of both the chemical and molecular methods is proven to be the best strategy for adulteration detection. Moreover, CRISPR diagnosis tools equipped with multiplexing techniques may be implemented for screening adulteration from herbal drugs, this will play a crucial role in herbal product authentication in the future.

Multi-level authentication for security in cloud using improved quantum key distribution.

Cloud computing is an on-demand virtual-based technology to develop, configure, and modify applications online through the internet. It enables the users to handle various operations such as storage, back-up, and recovery of data, data analysis, delivery of software applications, implementation of new services and applications, hosting websites and blogs, and streaming of audio and video files. Thereby, it provides us many benefits although it is backlashed due to problems related to cloud security like data leakage, data loss, cyber attacks, etc. To address the security concerns, researchers have developed a variety of authentication mechanisms. This means that the authentication procedure used in the suggested method is multi-levelled. As a result, a better QKD method is offered to strengthen cloud security against different types of security risks. Key generation for enhanced QKD is based on the ABE public key cryptography approach. Here, an approach named CPABE is used in improved QKD. The Improved QKD scored the reduced KCA attack ratings of 0.3193, this is superior to CMMLA (0.7915), CPABE (0.8916), AES (0.5277), Blowfish (0.6144), and ECC (0.4287), accordingly. Finally, this multi-level authentication using an improved QKD approach is analysed under various measures and validates the enhancement over the state-of-the-art models.

Secure and privacy improved cloud user authentication in biometric multimodal multi fusion using blockchain-based lightweight deep instance-based DetectNet.

This research introduces an innovative solution addressing the challenge of user authentication in cloud-based systems, emphasizing heightened security and privacy. The proposed system integrates multimodal biometrics, deep learning (Instance-based learning-based DetectNet-(IL-DN), privacy-preserving techniques, and blockchain technology. Motivated by the escalating need for robust authentication methods in the face of evolving cyber threats, the research aims to overcome the struggle between accuracy and user privacy inherent in current authentication methods. The proposed system swiftly and accurately identifies users using multimodal biometric data through IL-DN. To address privacy concerns, advanced techniques are employed to encode biometric data, ensuring user privacy. Additionally, the system utilizes blockchain technology to establish a decentralized, tamper-proof, and transparent authentication system. This is reinforced by smart contracts and an enhanced Proof of Work (PoW) mechanism. The research rigorously evaluates performance metrics, encompassing authentication accuracy, privacy preservation, security, and resource utilization, offering a comprehensive solution for secure and privacy-enhanced user authentication in cloud-based environments. This work significantly contributes to filling the existing research gap in this critical domain.

Microfluidics identify moso bamboo and henon bamboo by leaf protoplast subpopulations with single-cell analysis.

Current techniques identifying herbal medicine species require marker labeling or lack systematical accuracy (expert authentication). There is an emerging interest in developing an accurate and label-free tool for herbal medicine authentication. Here, a high-resolution microfluidic-based method is developed for identifying herbal species by protoplast subpopulations. Moso bamboo and henon bamboo are used as a model to be differentiated based on protoplast. Their biophysical properties factors are characterized to be 7.09 (± 0.39) × 108 V/m2 and 6.54 (± 0.26) × 108 V/m2, respectively. Their biophysical distributions could be distinguished by the Cramér-von Mises criterion with a 94.60% confidence level. The subpopulations of each were compared with conventional flow cytometry indicating the existence of subpopulations and the differences between the two species. The subsets divided by a biophysical factor of 8.05(± 0.51) × 108 V/m2 suggest good consistency with flow cytometry. The work demonstrated the possibility of microfluidics manipulation on protoplast for medication safety use taking advantage of dielectrophoresis. The device is promising in developing a reliable and accurate way of identifying herbal species with difficulties in authentication.

Development and validation of a noninvasive prediction model for significant hepatic liver fibrosis in Chinese patients with autoimmune hepatitis.

INTRODUCTION AND OBJECTIVES: Autoimmune hepatitis (AIH) is a prevalent noninfectious liver disease. However, there is currently a lack of noninvasive tests appropriate for evaluating liver fibrosis in AIH patients. The objective of this study was to develop and validate a predictive model for noninvasive assessment of significant liver fibrosis (S ≥ 2) in patients to provide a reliable method for evaluating liver fibrosis in individuals with AIH. MATERIALS AND METHODS: The clinical data of 374 AIH patients were analyzed. A prediction model was established through logistic regression in the training set, and bootstrap method was used to validate the models internally. In addition, the clinical data of 109 AIH patients were collected for external verification of the model.The model was expressed as a nomogram, and area under the curve (AUC) of the receiver operating characteristic (ROC), calibration curve, and decision curve analysis were used to evaluate the accuracy of the prediction model. RESULTS: Logistic regression analysis revealed that age, platelet count (PLT), and the A/G ratio were identified as independent risk factors for liver fibrosis in AIH patients (P < 0.05). The diagnostic model that was composed of age, PLT and A/G was superior to APRI and FIB-4 in both the internal validation (0.872, 95%CI: 0.819-0.924) and external validation (0.829, 95%CI: 0.753-0.904). CONCLUSIONS: Our predictive model can predict significant liver fibrosis in AIH patients more accurately, simply, and noninvasively.

Preparation of thermochromic ink from anthocyanidin-encapsulated alginate nanoparticles for anticounterfeiting applications.

In order to make commercial products less vulnerable to counterfeiting, thermochromic inks have proven to be a viable authentication strategy. Herein, we developed a thermochromic ink for authentication by combining an anthocyanidin (ACYD) extract with alginate (ALG). To increase the anthocyanidin/alginate ink stability, a mordant (ferrous sulfate) was employed to tie up the anthocyanidin biomolecules with alginate. ACYD was extracted from red-cabbage and then immobilized into alginate to serve as an environmentally friendly spectroscopic probe. Thermochromic composite inks (ACYD@ALG) were made by adjusting the content of anthocyanidin. A homogenous blue film (608 nm) was printed on a paper surface and investigated by the CIE Lab coordinate system. The blue color transformed into reddish (477 nm) when heated from 35°C to 65°C. Nanoparticles (NPs) of anthocyanidin/mordant (ACYD/M) were examined for their size and morphology to indicate diameters of 80-90 nm, whereas the ACYD/M-encapsulated alginate nanoparticles showed diameters of 120-150 nm. Multiple analytical techniques were utilized to examine the printed papers. The mechanical and rheological performance of both stamped sheets and ink fluid were explored. The cytotoxicity and antimicrobial efficacy of ink (ACYD@ALG) were investigated.

Lightweight Hash-Based Authentication Protocol for Smart Grids.

Smart grids integrate information and communications technology into the processes of electricity production, transportation, and consumption, thereby enabling interactions between power suppliers and consumers to increase the efficiency of the power grid. To achieve this, smart meters (SMs) are installed in households or buildings to measure electricity usage and allow power suppliers or consumers to monitor and manage it in real time. However, SMs require a secure service to address malicious attacks during memory protection and communication processes and a lightweight communication protocol suitable for devices with computational and communication constraints. This paper proposes an authentication protocol based on a one-way hash function to address these issues. This protocol includes message authentication functions to address message tampering and uses a changing encryption key for secure communication during each transmission. The security and performance analysis of this protocol shows that it can address existing attacks and provides 105,281.67% better computational efficiency than previous methods.

UAV-Based Secure Data Communication: Multilevel Authentication Perspective.

An Internet of Things (IoT) system for managing and coordinating unmanned aerial vehicles (UAVs) has revolutionized the industrial sector. The largest issue with the design of the Internet of UAVs (IoUAV) is security. Conspicuously, the novel contribution of the proposed work is to develop a layered authentication approach to facilitate safe IoUAV communication. Specifically, four modules, including the pre-deployment module, user registration module, login module, and authentication module, form the basis of security analysis. In the proposed technique, UAVs are added to the IoUAV registry. The next step is the user registration module, where people are registered with the UAV so they may access the information in real time. In the login module, the user connects with the server for data transmission. Finally, in the authentication module, all entities, including users, servers, and UAVs, are authenticated to ensure secure data communication. The proposed method achieves peak performance as compared to the state-of-the-art techniques in terms of statistical parameters of latency (3.255s), throughput (90.15%), and packet loss (8.854%).

A Blockchain-Based Authentication Mechanism for Enhanced Security.

Passwords are the first line of defence against preventing unauthorised access to systems and potential leakage of sensitive data. However, the traditional reliance on username and password combinations is not enough protection and has prompted the implementation of technologies such as two-factor authentication (2FA). While 2FA enhances security by adding a layer of verification, these techniques are not impervious to threats. Even with the implementation of 2FA, the relentless efforts of cybercriminals present formidable obstacles in securing digital spaces. The objective of this work is to implement blockchain technology as a form of 2FA. The findings of this work suggest that blockchain-based 2FA methods could strengthen digital security compared to conventional 2FA methods.

An Experimental Performance Assessment of Galileo OSNMA.

We present Galileo Open Service Navigation Message Authentication (OSNMA) observed operational information and key performance indicators (KPIs) from the analysis of a ten-day-long dataset collected in static open-sky conditions in southern Finland and using our in-house-developed OSNMA implementation. In particular, we present a timeline with authentication-related events, such as authentication status and type, dropped navigation pages, and failed cyclic redundancy checks. We also report other KPIs, such as the number of simultaneously authenticated satellites over time, time to first authenticated fix, and percentage of authenticated fixes, and we evaluate the accuracy of the authenticated position solution. We also study how satellite visibility affects these figures. Finally, we analyze situations where it was not possible to reach an authenticated fix, and offer our findings on the observed patterns.

Two-Layered Multi-Factor Authentication Using Decentralized Blockchain in an IoT Environment.

Internet of Things (IoT) technology is evolving over the peak of smart infrastructure with the participation of IoT devices in a wide range of applications. Traditional IoT authentication methods are vulnerable to threats due to wireless data transmission. However, IoT devices are resource- and energy-constrained, so building lightweight security that provides stronger authentication is essential. This paper proposes a novel, two-layered multi-factor authentication (2L-MFA) framework using blockchain to enhance IoT devices and user security. The first level of authentication is for IoT devices, one that considers secret keys, geographical location, and physically unclonable function (PUF). Proof-of-authentication (PoAh) and elliptic curve Diffie-Hellman are followed for lightweight and low latency support. Second-level authentication for IoT users, which are sub-categorized into four levels, each defined by specific factors such as identity, password, and biometrics. The first level involves a matrix-based password; the second level utilizes the elliptic curve digital signature algorithm (ECDSA); and levels 3 and 4 are secured with iris and finger vein, providing comprehensive and robust authentication. We deployed fuzzy logic to validate the authentication and make the system more robust. The 2L-MFA model significantly improves performance, reducing registration, login, and authentication times by up to 25%, 50%, and 25%, respectively, facilitating quicker cloud access post-authentication and enhancing overall efficiency.

Satellite Navigation Message Authentication in GNSS: Research on Message Scheduler for SBAS L1.

SBAS is mainly used in civil aviation and navigation, and will be applied to autonomous driving in the future. Given the open signal format of the Satellite Based Augmentation System (SBAS), which exposes security threats such as spoofing attacks, the utilization of SBAS navigation message authentication technology can improve the SBAS anti-spoofing ability from the system side. SBAS message authentication technology has become the future direction of SBAS system development. However, during the initial design of SBAS on L1, message authentication technology was not considered, and the addition of authentication messages will lead to further strain on existing message bandwidth resources. Therefore, in response to the issue of insufficient bandwidth resources after adding authentication messages to SBAS L1, a study on message scheduler for SBAS L1 authentication was conducted. A fixed time sequence dynamic message scheduler for incorporating authentication messages was proposed. This scheduler reduces the frequency of broadcasting clock error parameters to mitigate the impact of adding authentication messages. Furthermore, an optimized fixed time sequence dynamic message scheduler based on SBAS clock error messages was introduced. The results show that the message scheduler can not only improve the flexibility of SBAS message broadcasting, but also shorten the update interval of various types of messages under the premise of meeting the maximum update interval requirement. With minimal impact on the maximum message update interval, it improves the integrity, authenticity, and availability of messages. This approach can increase the effective message ratio in SBAS to over 91%, and the optimal solution reduces the initial user positioning time to 26 s.

Decentralized IoT Data Authentication with Signature Aggregation.

The rapid expansion of the Internet of Things (IoT) has introduced significant challenges in data authentication, necessitating a balance between scalability and security. Traditional approaches often rely on third parties, while blockchain-based solutions face computational and storage bottlenecks. Our novel framework employs edge aggregating servers and Ethereum Layer 2 rollups, offering a scalable and secure IoT data authentication solution that reduces the need for continuous, direct interaction between IoT devices and the blockchain. We utilize and compare the Nova and Risc0 proving systems for authenticating batches of IoT data by verifying signatures, ensuring data integrity and privacy. Notably, the Nova prover significantly outperforms Risc0 in proving and verification times; for instance, with 10 signatures, Nova takes 3.62 s compared to Risc0's 369 s, with this performance gap widening as the number of signatures in a batch increases. Our framework further enhances data verifiability and trust by recording essential information on L2 rollups, creating an immutable and transparent record of authentication. The use of Layer 2 rollups atop a permissionless blockchain like Ethereum effectively reduces on-chain storage costs by approximately 48 to 57 times compared to direct Ethereum use, addressing cost bottlenecks efficiently.