Investigation of the Volatile Profile of Red Jujube by Using GC-IMS, Multivariate Data Analysis, and Descriptive Sensory Analysis.

peer reviewedThe aroma characteristics of six red jujube cultivars (Jinchang-'JC', Junzao-'JZ', Huizao-'HZ', Qiyuexian-'QYX', Hetiandazao-'HTDZ', and Yuanzao-'YZ'), cultivated in Xinjiang Province, China, were studied by E-nose and GC-IMS. The presence of acetoin, E-2-hexanol, hexanal, acetic acid, and ethyl acetate played an important role in the classification results. JC, JZ, HZ, and YZ were different from others, while QYX and HTDZ were similar to each other. HZ had the most abundant specific VOCs, including linalool, nonanoic acid, methyl myristoleate, 2-acetylfuran, 1-octen-3-one, E-2-heptenal, 2-heptenone, 7-octenoic acid, and 2-pentanone. HZ had higher intensity in jujube ID, floral, sweet, and fruity attributes. Correlation analysis showed that jujube ID (identity) might be related to phenylacetaldehyde and isobutanoic acid that formed by the transamination or dehydrogenation of amino acids; meanwhile, the sweet attribute was correlated with amino acids, including threonine, glutamic acid, glycine, alanine, valine, leucine, tyrosine, phenylalanine, lysine, histidine, and arginine

High-Throughput Screen Reveals sRNAs Regulating crRNA Biogenesis by Targeting CRISPR Leader to Repress Rho Termination

Discovery of CRISPR-Cas systems is one of paramount importance in the field of microbiology. Currently, how CRISPR-Cas systems are finely regulated remains to be defined. Here we use small regulatory RNA (sRNA) library to screen sRNAs targeting type I-F CRISPR-Cas system through proximity ligation by T4 RNA ligase and find 34 sRNAs linking to CRISPR loci. Among 34 sRNAs for potential regulators of CRISPR, sRNA pant463 and PhrS enhance CRISPR loci transcription, while pant391 represses their transcription. We identify PhrS as a regulator of CRISPR-Cas by binding CRISPR leaders to suppress Rho-dependent transcription termination. PhrS-mediated anti-termination facilitates CRISPR locus transcription to generate CRISPR RNA (crRNA) and subsequently promotes CRISPR-Cas adaptive immunity against bacteriophage invasion. Furthermore, this also exists in type I-C/-E CRISPR-Cas, suggesting general regulatory mechanisms in bacteria kingdom. Our findings identify sRNAs as important regulators of CRISPR-Cas, extending roles of sRNAs in controlling bacterial physiology by promoting CRISPR-Cas adaptation priming

A Congestion Control Strategy for Power Scale-Free Communication Network

The scale-free topology of power communication network leads to more data flow in less hub nodes, which can cause local congestion. Considering the differences of the nodes’ delivery capacity and cache capacity, an integrated routing based on the communication service classification is proposed to reduce network congestion. In the power communication network, packets can be classified as key operational services (I-level) and affairs management services (II-level). The shortest routing, which selects the path of the least hops, is adopted to transmit I-level packets. The load-balanced global dynamic routing, which uses the node’s queue length and delivery capacity to establish the cost function and chooses the path with minimal cost, is adopted to transmit II-level packets. The simulation results show that the integrated routing has a larger critical packet generation rate and can effectively reduce congestion

Design of a multi-epitope vaccine against goatpox virus using an immunoinformatics approach

IntroductionGoatpox, a severe infectious disease caused by goatpox virus (GTPV), leads to enormous economic losses in the livestock industry. Traditional live attenuated vaccines cause serious side effects and exist a risk of dispersal. Therefore, it is urgent to develop efficient and safer vaccines to prevent and control of GTPV.MethodsIn the present study, we are aimed to design a multi-epitope subunit vaccine against GTPV using an immunoinformatics approach. Various immunodominant cytotoxic T lymphocytes (CTL) epitopes, helper T lymphocytes (HTL) epitopes, and B-cell epitopes from P32, L1R, and 095 proteins of GTPV were screened and liked by the AAY, GPGPG, and KK connectors, respectively. Furthermore, an adjuvant β-defensin was attached to the vaccine’s N-terminal using the EAAAK linker to enhance immunogenicity.ResultsThe constructed vaccine was soluble, non-allergenic and non-toxic and exhibited high levels of antigenicity and immunogenicity. The vaccine’s 3D structure was subsequently predicted, refined and validated, resulting in an optimized model with a Z-value of -3.4. Molecular docking results demonstrated that the vaccine had strong binding affinity with TLR2(-27.25 kcal/mol), TLR3(-39.84 kcal/mol), and TLR4(-59.42 kcal/mol). Molecular dynamics simulation results indicated that docked vaccine-TLR complexes were stable. Immune simulation analysis suggested that the vaccine can induce remarkable increase in antibody titers of IgG and IgM, higher levels of IFN-γ and IL-2.ConclusionThe designed GTPV multi-epitope vaccine is structurally stable and can induce robust humoral and cellular immune responses, which may be a promising vaccine candidate against GTPV

A New Method for the Rapid Determination of Fire Disturbance Events Using GEE and the VCT Algorithm—A Case Study in Southwestern and Northeastern China

Forest fires are major disturbances in forest ecosystems. The rapid detection of the spatial and temporal characteristics of fires is essential for formulating targeted post-fire vegetation restoration measures and assessing fire-induced carbon emissions. We propose an accurate and efficient framework for extracting the spatiotemporal characteristics of fires using vegetation change tracker (VCT) products and the Google Earth Engine (GEE) platform. The VCT was used to extract areas of persistent forest and forest disturbance patches from Landsat images of Xichang and Muli, Liangshan prefecture, Sichuan province in southwestern China and Huma, Heilongjiang province, in northeastern China. All available Landsat images in the GEE platform in a year were normalized using the VCT-derived persisting forest mask to derive three standardized vegetation indices (normalized burn ratio (NBRr), normalized difference moisture index (NDMIr), and normalized difference vegetation index (NDVIr)). Historical forest disturbance events in Xichang were used to train two decision trees using the C4.5 data mining tool. The differenced NBRr, NDMIr, and NDVIr (dNBRr, dNDMIr, and dNDVIr) were obtained by calculating the difference in the index values between two temporally adjacent images. The occurrence time of disturbance events were extracted using the thresholds identified by decision tree 1. The use of all available images in GEE narrowed the disturbance occurrence time down to 16 days. This period was extended if images were not available or had cloud cover. Fire disturbances were distinguished from other disturbances by comparing the dNBRr, dNDMIr, and dNDVIr values with the thresholds identified by decision tree 2. The results showed that the proposed framework performed well in three study areas. The temporal accuracy for detecting disturbances in the three areas was 94.33%, 90.33%, and 89.67%, the classification accuracy of fire and non-fire disturbances was 85.33%, 89.67%, and 83.67%, and the Kappa coefficients were 0.71, 0.74, and 0.67, respectively. The proposed framework enables the efficient and rapid extraction of the spatiotemporal characteristics of forest fire disturbances using frequent Landsat time-series data, GEE, and VCT products. The results can be used in forest fire disturbance databases and to implement targeted post-disturbance vegetation restoration practices