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Porcine hemagglutinating encephalomyelitis virus (PHEV), porcine pseudorabies virus (PRV), and classical swine fever virus (CSFV) are currently prevalent worldwide and cause similar neurological symptoms in infected pigs. It is very important to establish a detection method that can rapidly and accurately detect and differentiate these three viruses. Targeting the PHEV N gene, PRV gB gene, and CSFV 5′ untranslated region (5′UTR), three pairs of specific primers and probes were designed, and a triplex crystal digital reverse transcription-PCR (cdRT-PCR) was developed to detect PHEV, PRV, and CSFV. The results indicated that this assay had high sensitivity, and the limitation of detection (LODs) for PHEV, PRV, and CSFV were 4.812, 4.047, and 5.243 copies/reaction, respectively, which was about 50 times higher than that of multiplex real-time quantitative RT-PCR (RT-qPCR). This assay showed good specificity, without cross-reaction with other important swine pathogens, i.e., FMDV, PRRSV, PEDV, SIV, TGEV, PoRV, and PCV2. This assay had high repeatability, with intra-assay coefficients of variation (CVs) of 0.73–1.87%, and inter-assay CVs of 0.57–2.95%. The developed assay was used to test 1,367 clinical tissue samples from Guangxi province in China, and the positive rates of PHEV, PRV, and CSFV were 3.44% (47/1,367), 1.24% (17/1,367), and 1.90% (26/1,367), respectively, with a coincidence rate of 98.98% and a Kappa value of 0.94 to the reference multiplex RT-qPCR. The established triplex cdRT-PCR was a highly rapid, sensitive, and accurate assay to detect and differentiate PHEV, PRV, and CSFV. [ABSTRACT FROM AUTHOR]

Canine coronavirus (CCoV), canine respiratory coronavirus (CRCoV), canine adenovirus type 2 (CAV-2), and canine norovirus (CNV) are important pathogens for canine viral gastrointestinal and respiratory diseases. Especially, co-infections with these viruses exacerbate the damages of diseases. In this study, four pairs of primers and probes were designed to specifically amplify the conserved regions of the CCoV M gene, CRCoV N gene, CAV-2 hexon gene, and CNV RdRp gene. After optimizing different reaction conditions, a quadruplex RT-qPCR was established for the detection of CCoV, CRCoV, CAV-2, and CNV. The specificity, sensitivity, and repeatability of the established assay were evaluated. Then, the assay was used to test 1688 clinical samples from pet hospitals in Guangxi province of China during 2022–2024 to validate its clinical applicability. In addition, these samples were also assessed using the reported reference RT-qPCR assays, and the agreements between the developed and reference assays were determined. The results indicated that the quadruplex RT-qPCR could specifically test only CCoV, CRCoV, CAV-2, and CNV, without cross-reaction with other canine viruses. The assay had high sensitivity with limits of detection (LODs) of 1.0 × 102 copies/reaction for CCoV, CRCoV, CAV-2, and CNV. The repeatability was excellent, with intra-assay variability of 0.19–1.31% and inter-assay variability of 0.10–0.88%. The positivity rates of CCoV, CRCoV, CAV-2, and CNV using the developed assay were 8.59% (145/1688), 8.65% (146/1688), 2.84% (48/1688), and 1.30% (22/1688), respectively, while the positivity rates using the reference assays were 8.47% (143/1688), 8.53% (144/1688), 2.78% (47/1688), and 1.24% (21/1688), respectively, with agreements of more than 99.53% between two methods. In conclusion, a quadruplex RT-qPCR with high sensitivity, specificity, and repeatability was developed for rapid, and accurate detection of CCoV, CRCoV, CAV-2, and CNV. [ABSTRACT FROM AUTHOR]

Porcine astrovirus (PoAstV), porcine sapovirus (PoSaV), porcine norovirus (PoNoV), and porcine rotavirus A (PoRVA) are newly discovered important porcine diarrhea viruses with a wide range of hosts and zoonotic potential, and their co-infections are often found in pig herds. In this study, the specific primers and probes were designed targeting the ORF1 gene of PoAstV, PoSaV, and PoNoV, and the VP6 gene of PoRVA. The recombinant standard plasmids were constructed, the reaction conditions (concentration of primers and probes, annealing temperature, and reaction cycle) were optimized, and the specificity, sensitivity, and reproducibility were analyzed to establish a quadruplex real-time quantitative RT-PCR (RT-qPCR) assay for the detection of these four diarrheal viruses. The results demonstrated that the assay effectively tested PoAstV, PoSaV, PoNoV, and PoRVA without cross-reactivity with other swine viruses, and had limits of detection (LODs) of 138.001, 135.167, 140.732, and 132.199 (copies/reaction) for PoAstV, PoSaV, PoNoV, and PoRVA, respectively, exhibiting high specificity and sensitivity. Additionally, it displayed good reproducibility, with coefficients of variation (CVs) of 0.09–1.24% for intra-assay and 0.08–1.03% for inter-assay. The 1578 clinical fecal samples from 14 cities in Guangxi Province, China, were analyzed via the developed assay. The results indicated that the clinical samples from Guangxi Province exhibited the prevalence of PoAstV (35.93%, 567/1578), PoSaV (8.37%, 132/1578), PoNoV (2.98%, 47/1578), and PoRVA (14.32%, 226/1578), and had a notable incidence of mixed infections of 18.31% (289/1578). Simultaneously, the 1578 clinical samples were analyzed with the previously established assays, and the coincidence rates of these two approaches exceeded 99.43%. This study developed an efficient and precise diagnostic method for the detection and differentiation of PoAstV, PoSaV, PoNoV, and PoRVA, enabling the successful diagnosis of these four diseases. [ABSTRACT FROM AUTHOR]

Simple Summary: African swine fever virus (ASFV), porcine reproductive and respiratory syndrome virus (PRRSV), classical swine fever virus (CSFV), and porcine pseudorabies virus (PRV) are important pathogens circulating in pig herds in many countries. Pigs infected with these viruses show similar signs, such as increased body temperature and loss of appetite, and are prone to respiratory symptoms, digestive symptoms, neurological symptoms, and/or abortion in pregnant sows. To accurately detect these viruses and differentially diagnose these diseases, four pairs of specific primers and TaqMan probes were designed aiming at the B646L (p72) gene of ASFV, the 5′ untranslated region (5′UTR) of CSFV, the ORF6 gene of PRRSV, and the gB gene of PRV. After optimizing the reaction conditions, a multiplex real-time quantitative RT-PCR (RT-qPCR) assay was developed for the differential detection of ASFV, CSFV, PRRSV, and PRV. The assay was further validated to test 3116 clinical samples, and the positivity rates of ASFV, CSFV, PRRSV, and PRV were 10.84% (338/3116), 0.80% (25/3116), 14.92% (465/3116), and 1.38% (43/3116), respectively. The assay provides a highly specific, sensitive, and reproducible method for the detection of ASFV, CSFV, PRRSV, and PRV. African swine fever virus (ASFV), classical swine fever virus (CSFV), porcine reproductive and respiratory syndrome virus (PRRSV), and porcine pseudorabies virus (PRV) induce similar clinical signs in infected pigs, including hyperthermia, anorexia, hemorrhage, respiratory distress, neurological symptoms, and/or abortions in pregnant sows. The differential diagnosis of these diseases relies on laboratory examinations. In this study, a quadruplex RT-qPCR was established using four pairs of specific primers and probes aimed at the B646L (p72) gene of ASFV, the 5′ untranslated region (5′UTR) of CSFV, the ORF6 gene of PRRSV, and the gB gene of PRV for the detection and differentiation of ASFV, CSFV, PRRSV, and PRV. The assay exhibited great sensitivity with limits of detection (LODs) of 134.585, 139.831, 147.076, and 142.331 copies/reaction for ASFV, CSFV, PRRSV, and PRV, respectively. The assay exclusively identified ASFV, CSFV, PRRSV, and PRV, yielding negative results for the other control swine viruses used in this study. The intra-assay and inter-assay coefficients of variation (CVs) were not higher than 1.12%, indicating good reproducibility of the assay. The quadruplex RT-qPCR assay was used to analyze 3116 clinical tissue samples from pigs in Guangxi province, China, from April 2023 to September 2024. ASFV, CSFV, PRRSV, and PRV had positivity rates of 10.84% (338/3116), 0.80% (25/3116), 14.92% (465/3116), and 1.38% (43/3116), respectively, demonstrating a coincidence rate of ≥99.45% with the previously described RT-qPCR assays, which were also used to test these same samples. The established assay was rapid, sensitive, and accurate in detecting and differentiating ASFV, CSFV, PRRSV, and PRV. [ABSTRACT FROM AUTHOR]

Simple Summary: Canine coronavirus (CCoV) is an important gastrointestinal pathogen in dogs. It is endemic worldwide and poses a significant risk to canine health. Here, the prevalence and genetic characteristics of CCoV in pet dogs in Guangxi Province, China, were examined. Sixty-five CCoV-positive samples were selected for amplifying and sequencing S, M, and N genes to analyze the similarity, recombination, and evolution of CCoV strains. The results indicated that the positivity rate of CCoV was 8.43% and showed a close correlation with age and season in pet dog infections. The strains obtained in Guangxi Province belonged to the CCoV-IIa and CCoV-IIb subtypes, and recombination and mutations were found in Guangxi's CCoV strains. These provided insights into the genetic characteristics of CCoV in Guangxi Province, China. Canine coronavirus (CCoV) is an important gastrointestinal pathogen that causes serious harm to pet dogs worldwide. Here, 1791 clinical samples were collected from pet dogs in different pet hospitals in Guangxi Province, southern China, for the 2021–2024 period and detected for CCoV by a multiplex RT-qPCR. The results showed that 8.43% (151/1791) of samples were positive for CCoV. Sixty-five positive samples were selected to amplify, sequence, and analyze S, M, and N genes. A sequence comparison revealed that the nucleotide and amino acid similarities of the S, M, and N genes were 94.86% and 94.62%, 96.85% and 97.80%, and 96.85% and 97.80%, respectively. Phylogenetic analysis indicated that 65 CCoV strains obtained in this study belonged to the CCoV-II genotype, of which 56 CCoV strains belonged to the CCoV-IIa subtype and 9 CCoV strains belonged to the CCoV-IIb subtype. A potential recombination event analysis of S gene sequences indicated that two CCoV strains, i.e., GXBSHM0328-34 and GXYLAC0318-35, have recombination signals. A Bayesian analysis indicated that the evolutionary rates of the S, M, and N genes were 1.791 × 10−3, 6.529 × 10−4, and 4.775 × 10−4 substitutions/site/year, respectively. The population size grew slowly before 1980 and then began to shrink slowly; it then shrank rapidly in 2005 and expanded sharply in 2020, leveling off thereafter. These results indicated the CCoV strains prevalent in Guangxi Province, southern China, showed a high level of genetic diversity and maintained continuous variation among clinical epidemic strains. [ABSTRACT FROM AUTHOR]

Simple Summary: Feline coronavirus (FCoV), as one of the important pathogens of feline viral gastroenteritis, has been attracting great attention. In this study, a total of 1869 rectal and nasal swabs, feces, and ascites samples from Guangxi province in southern China were collected during 2021–2024 and a positivity rate of 17.66% (330/1869) for FCoV was found. The nucleotide and amino acid homologies of S, M, and N genes were 81.2–99.6% and 70.2–99.5%, 89.9–100% and 91.6–100%, and 90.1–100% and 91.5–100%, respectively. All 63 FCoV strains obtained in this study belonged to type I FCoV (FCoV-I). Recombinant signals were detected in the S gene of FCoV strains GXLZ03-2022 and GXLZ08-2022, and in the CCoV strain GD/2020/X9. The results indicate that FCoV is still prevalent in Guangxi province, and the prevalent FCoV strains show high genetic diversity and novel epidemic characteristics. Feline coronavirus (FCoV), as one of the important pathogens of feline viral gastroenteritis, has been attracting great attention. A total of 1869 rectal and nasal swabs, feces, and ascites samples were collected from eight regions in Guangxi province during 2021–2024. The multiplex RT-qPCR established in our laboratory was used to test these samples for FCoV, and 17.66% (330/1869) of the samples were positive for FCoV. The S, M, and N genes of 63 FCoV-positive samples were amplified and sequenced, and the genetic and evolutionary characteristics were analyzed. Similarity analysis showed that the nucleotide and amino acid homologies of S, M, and N genes were 81.2–99.6% and 70.2–99.5%, 89.9–100% and 91.6–100%, and 90.1–100% and 91.5–100%, respectively. Phylogenetic analysis revealed that all 63 FCoV strains, based on S gene sequences, belonged to type I FCoV (FCoV-I), and were clustered with Chinese strains and the Netherlands UU strains. Recombinant signals were detected in the S gene of strains GXLZ03-2022, GXLZ08-2022, and CCoV GD/2020/X9. The results suggest that FCoV is still prevalent in the Guangxi province of southern China, and the prevalent FCoV strains show high genetic diversity and novel epidemic characteristics. [ABSTRACT FROM AUTHOR]

The Runchang‐Tongbian (RCTB) formula is a traditional Chinese medicine (TCM) formula consisting of four herbs, namely Cannabis Fructus (Huomaren), Rehmanniae Radix (Dihuang), Atractylodis Macrocephalae Rhizoma (Baizhu), and Aurantii Fructus (Zhiqiao). It is widely used clinically because of its beneficial effect on constipation. However, its strong bitter taste leads to poor patient compliance. The bitter components of TCM compounds are complex and numerous, and inhibiting the bitter taste of TCM has become a major clinical challenge. Here, we use ultra‐high‐performance liquid chromatography coupled with mass spectrometry (UPLC–MS) and high‐resolution mass spectrometry to identify 59 chemical components in the TCM compound RCTB formula. Next, four bitter taste receptors, TAS2R39, TAS2R14, TAS2R7, and TAS2R5, which are tightly bound to the compounds in RCTB, were screened as molecular docking receptors using the BitterX database. The top‐three‐scoring receptor‐small‐molecule complexes for each of the four receptors were selected for molecular dynamics simulation. Finally, seven bitter components were identified, namely six flavonoids (rhoifolin, naringin, poncirin, diosmin, didymin, and narirutin) and one phenylpropanoid (purpureaside C). Thus, we proposed a new method for identifying the bitter components in TCM compounds, which provides a theoretical reference for bitter taste inhibition in TCM compounds. [ABSTRACT FROM AUTHOR]

Image encryption technology is an essential measure to protect image information from illegal access. A new probabilistic image encryption algorithm with 480-bit external secret key was proposed in this paper. A chaotic pseudo-random generator is employed to produce the equivalent keys. Then the plain image is covered by the equivalent keys under exclusive or operation to obtain a pending sequence. The pending sequence is transformed into a cipher sequence by arithmetic operations based on GF(257). Finally, the cipher sequence is also covered by the equivalent keys to obtain the cipher image. In the transformation, a 1024-bit pseudo-random number sequence is introduced as the base numbers for the power operation to assist the encryption. The proposed image encryption algorithm has the characteristic of probabilistic encryption. Encrypting the same plain image with the same secret key will result in completely different cipher images. Experiments show that the proposed algorithm has the low time complexity (9.67pi/bit) and space complexity (13.07bit/bit), and high sensitivities on the auxiliary sequence (49.94%), secret key (BACI 26.7698% typically), and plain image (BACI 26.7845% typically), and is a preferred image encryption scheme for commercial and personal image information security. [ABSTRACT FROM AUTHOR]

The variant porcine epidemic diarrhea virus (PEDV) has caused considerable economic losses to the global pig industry since 2010. In this study, a total of 5859 diarrhea samples were collected from different pig farms in China's Guangxi province during January 2020 and March 2024 and tested for PEDV using RT-qPCR. The positivity rate of PEDV was 11.90% (697/5859). Ninety-two PEDV-positive samples were selected based on sampling time, and the sampling region for amplification, sequencing, and analysis of the S1, M, and N genes. Phylogenetic analysis of the S1 gene revealed that all strains from Guangxi province were distributed in three subgroups, i.e., 81.5% (75/92) in the G2a subgroup, 4.3% (4/92) in the G2b subgroup, and 14.1% (13/92) in the G2c subgroup. The sequence analysis revealed that the S1 gene sequences from Guangxi province had higher homology with the variant strains than with the classical strains, showing as high as 99.2% with the variant strain AJ1102 and only 94.3% with the classical strain CV777. Recombination analysis revealed that the GX-BS08-2023 strain (G2c) from Guangxi province originated from inter-lineage recombination between the GX-BS09-2023 (G2a) and CH-JN547228-2011 (G1a) strains. In addition, the S1 gene of the G2a and G2b subgroup strains shared many mutations and insertions. There were common mutations of N143D and P235L in the G2a subgroup. Evolutionary analysis revealed that all Guangxi strains belonged to the G2 genotype. These strains have spread rapidly since the PEDV variant strains that emerged in 2010, weakened until 2021, and then remained stable. In conclusion, the results revealed the latest genetic evolution of circulating PEDV strains in Guangxi province in recent years, providing important information for preventing and controlling PEDV infection. Currently, the G2a subgroup strains are the predominant strains circulating in pig herds in Guangxi province, southern China. [ABSTRACT FROM AUTHOR]

Background: Canine coronavirus (CCoV), canine rotavirus (CRV), canine parvovirus (CPV), and canine distemper virus (CDV) cause gastroenteritis in dogs, and co-infections of these pathogens are common in China. In particular, CCoV and CRV are confirmed to have important zoonotic potential and cause public health issues. It is difficult to diagnose these diseases based only on clinical manifestations and pathological damage. Methods: In this study, four pairs of specific primers and probes targeting the CCoV M, CRV VP7, CPV VP2, and CDV N genes were designed. The reaction conditions, including the primer and probe concentrations, annealing temperatures, and reaction cycles, were optimized for the development of a quadruplex RT-qPCR for the detection of CCoV, CRV, CPV, and CDV. The assay was used to test 1028 clinical samples to validate its application. Results: A quadruplex RT-qPCR was successfully established for the differential detection of CCoV, CRV, CPV, and CDV, with good specificity, high sensitivity, and excellent repeatability. The assay could specifically detect CCoV, CRV, CPV, and CDV without cross-reactivity with the other canine viruses tested. It showed high sensitivity with limits of detection (LOD) of 1.1 × 102 copies/reaction for all four plasmid constructs. It showed excellent repeatability, with 0.05–0.90% intra-assay variation and 0.02–0.94% inter-assay variation. The 1028 clinical samples were tested using the quadruplex RT-qPCR and a reported reference RT-qPCR. The positivity rates of CCoV, CRV, CPV, and CDV were 9.53%, 0.97%, 25.68%, and 5.06% using the developed assay, and 9.05%, 0.88%, 25.68%, and 4.86% using the reference assay, with agreements higher than 99.32%. Conclusion: The results indicated that a rapid and accurate quadruplex RT-qPCR was developed for the detection and differentiation of CCoV, CRV, CPV, and CDV. [ABSTRACT FROM AUTHOR]

Porcine respiratory coronavirus (PRCoV), porcine reproductive and respiratory syndrome virus (PRRSV), swine influenza virus (SIV), and pseudorabies virus (PRV) are significant viruses causing respiratory diseases in pigs. Sick pigs exhibit similar clinical symptoms such as fever, cough, runny nose, and dyspnea, making it very difficult to accurately differentially diagnose these diseases on site. In this study, a quadruplex one-step reverse-transcription real-time quantitative PCR (RT-qPCR) for the detection of PRCoV, PRRSV, SIV, and PRV was established. The assay showed strong specificity, high sensitivity, and good repeatability. It could detect only PRCoV, PRRSV, SIV, and PRV, without cross-reactions with TGEV, PEDV, PRoV, ASFV, FMDV, PCV2, PDCoV, and CSFV. The limits of detection (LODs) for PRCoV, PRRSV, SIV, and PRV were 129.594, 133.205, 139.791, and 136.600 copies/reaction, respectively. The intra-assay and inter-assay coefficients of variation (CVs) ranged from 0.29% to 1.89%. The established quadruplex RT-qPCR was used to test 4909 clinical specimens, which were collected in Guangxi Province, China, from July 2022 to September 2023. PRCoV, PRRSV, SIV, and PRV showed positivity rates of 1.36%, 10.17%, 4.87%, and 0.84%, respectively. In addition, the previously reported RT-qPCR was also used to test these specimens, and the agreement between these methods was higher than 99.43%. The established quadruplex RT-qPCR can accurately detect these four porcine respiratory viruses simultaneously, providing an accurate and reliable detection technique for clinical diagnosis. [ABSTRACT FROM AUTHOR]

In this paper we investigate the dynamic multi-attribute group decision making problems, in which all the attribute values are provided by multiple decision makers at different periods. In order to increase the level of overall satisfaction for the final decision and deal with uncertainty, the attribute values are enhanced with generalized interval-valued trapezoidal fuzzy numbers to cope with the vagueness and indeterminacy. We first define the Dynamic Generalized Interval-valued Trapezoidal Fuzzy Numbers Weighted Geometric Aggregation (DGITFNWGA) operator and give an approach to determine the weights of periods, using the probability density function of Gamma distribution, and then a dynamic multi-attribute group decision making method is developed. The method proposed employs the Generalized Interval-valued Trapezoidal Fuzzy Numbers Hybrid Geometric Aggregation (GITFNHGA) operator to aggregate all individual decision information into the collective attribute values corresponding to each alternative at the same time period, and then utilizes the DGITFNWGA operator to aggregate the collective attribute values at different periods into the overall attribute values corresponding to each alternative and obtains the alternatives ranking, by which the optimal alternative can be determined. Finally, an illustrative example is given to verify the approach developed.