Analysis of Physicochemical Quality and Flavor Differences of Five Commercially Available Tiger Nut Oils with Different Processes Based on GC-IMS Technique

In order to evaluate the differences in physicochemical quality and flavor of commercially edible tiger nut oil, gas chromatography-ion mobility spectroscopy (GC-IMS) combined with principal component analysis was used to compare the volatile compounds in tiger nut oil from different extraction methods (physical pressing, hot pressing, subcritical extraction, high-pressure cold pressing and cold pressing). The GC-IMS results showed that 76 volatile compounds including 12 esters, 16 alcohols, 29 aldehydes, 9 ketones, 5 acids, 3 furans, 1 pyrazine and 1 sulfur compound were identified in the oil from different extraction methods. The relative content of aldehydes, esters and furans (53.54%, 13.06%, 5.41%) in the hot-pressing group were higher than others. The flavor differences of tiger nut oil from different processes were mainly derived from four key flavor substances as 1-octen-3-ol, nonanal, (E)-2-octenal and hexanal. The results of principal component analysis (PCA) showed that PC1 and PC2 were 48.7% and 30.1%, respectively, with a cumulative difference contribution of 78.8%, and the differences in volatile compounds between different processes were significant, which could be well distinguished by different extraction methods. The results of pearson correlation analysis showed that there was a significant positive correlation between b* value and acid value, peroxide value, p-malondialdehyde value and 1-octen-3-ol (0.57<r<0.88, P<0.05). The research could provide some reference value for the production and processing, theoretical research and quality inspection of commercial tiger nut oil

Bifurcation of Arabidopsis NLR Immune Signaling via Ca2+-Dependent Protein Kinases

Nucleotide-binding domain leucine-rich repeat (NLR) protein complexes sense infections and trigger robust immune responses in plants and humans. Activation of plant NLR resistance (R) proteins by pathogen effectors launches convergent immune responses, including programmed cell death (PCD), reactive oxygen species (ROS) production and transcriptional reprogramming with elusive mechanisms. Functional genomic and biochemical genetic screens identified six closely related Arabidopsis Ca2+-dependent protein kinases (CPKs) in mediating bifurcate immune responses activated by NLR proteins, RPS2 and RPM1. The dynamics of differential CPK1/2 activation by pathogen effectors controls the onset of cell death. Sustained CPK4/5/6/11 activation directly phosphorylates a specific subgroup of WRKY transcription factors, WRKY8/28/48, to synergistically regulate transcriptional reprogramming crucial for NLR-dependent restriction of pathogen growth, whereas CPK1/2/4/11 phosphorylate plasma membrane-resident NADPH oxidases for ROS production. Our studies delineate bifurcation of complex signaling mechanisms downstream of NLR immune sensors mediated by the myriad action of CPKs with distinct substrate specificity and subcellular dynamics

Case Report: Chronic hepatitis E virus Infection in an individual without evidence for immune deficiency

Chronic hepatitis E virus (HEV) infection occurs mainly in immunosuppressed populations. We describe an investigation of chronic HEV infection of genotype 3a in an individual without evidence for immune deficiency who presented hepatitis with significant HEV viremia and viral shedding. We monitored HEV RNA in plasma and stools, and assessed anti-HEV specific immune responses. The patient was without apparent immunodeficiency based on quantified results of white blood cell, lymphocyte, neutrophilic granulocyte, CD3+ T cell, CD4+ T cell, and CD8+ T cell counts and CD4/CD8 ratio, as well as total serum IgG, IgM, and IgA, which were in the normal range. Despite HEV specific cellular response and strong humoral immunity being observed, viral shedding persisted up to 109 IU/mL. After treatment with ribavirin combined with interferon, the indicators of liver function in the patient returned to normal, accompanied by complete suppression and clearance of HEV. These results indicate that HEV chronicity can also occur in individuals without evidence of immunodeficiency

Systematic Identification of Long Non-Coding RNAs under Allelopathic Interference of Para-Hydroxybenzoic Acid in S. lycopersicum

The importance of long noncoding RNAs (lncRNAs) in plant development has been established, but a systematic analysis of the lncRNAs expressed during plant allelopathy has not been carried out. We performed RNA-seq experiments on S. lycopersicum subjected to different levels of para-hydroxybenzoic acid (PHBA) stress during plant allelopathy and identified 61,729 putative lncRNAs. Of these, 7765 lncRNAs cis-regulated 5314 protein-coding genes (PGs). Among these genes, 1116 lncRNAs and 2239 PGs were involved in a complex web of transcriptome regulation, and we divided these genes into 12 modules. Within these modules, 458 lncRNAs and 975 target genes were found to be highly correlated. Additionally, 989 lncRNAs trans-regulated 1765 PGs, and we classified them into 11 modules, within which 335 lncRNAs were highly correlated with their 633 corresponding target genes. Only 98 lncRNAs in S. lycopersicum had homologs in the lncRNA database of Arabidopsis thaliana, all of which were affected by the PHBA treatments. MiRNAs that interacted with both mRNAs and lncRNAs were selected on the basis of weighted correlation network analysis (WGCNA) results to make lncRNA-miRNA-mRNA triplets. Our study presents a systematic identification of lncRNAs involved in plant allelopathy in S. lycopersicum and provides research references for future studies

Batch-Wise Permutation Feature Importance Evaluation and Problem-Specific Bigraph for Learn-to-Branch

The branch-and-bound algorithm for combinatorial optimization typically relies on a plethora of handcraft expert heuristics, and a research direction, so-called learn-to-branch, proposes to replace the expert heuristics in branch-and-bound with machine learning models. Current studies in this area typically use an imitation learning (IL) approach; however, in practice, IL often suffers from limited training samples. Thus, it has been emphasized that a small-dataset fast-training scheme for IL in learn-to-branch is worth studying, so that other methods, e.g., reinforcement learning, may be used for subsequent training. Thus, this paper focuses on the IL part of a mixed training approach, where a small-dataset fast-training scheme is considered. The contributions are as follows. First, to compute feature importance metrics so that the state-of-the-art bigraph representation can be effectively reduced for each problem type, a batch-wise permutation feature importance evaluation method is proposed, which permutes features within each batch in the forward pass. Second, based on the evaluated importance of the bigraph features, a reduced bigraph representation is proposed for each of the benchmark problems. The experimental results on four MILP benchmark problems show that our method improves branching accuracy by 8% and reduces solution time by 18% on average under the small-dataset fast-training scheme compared to the state-of-the-art bigraph-based learn-to-branch method. The source code is available online at GitHub

Factors Affecting the Natural Regeneration of the Larix principis-rupprechtii Mayr Plantations: Evidence from the Composition and Co-Occurrence Network Structure of Soil Bacterial Communities

Bacterial communities living in the soil can affect forests natural regeneration, but the effects of their composition and network inference on regeneration of Larix principis-rupprechtii Mayr plantations remain largely elusive. Therefore, the redundancy analysis and structure equations modeling of affecting elements for the regeneration of L. principis-rupprechtii plots including the diversity, composition and network structure of soil bacteria, topographic factors, light factors, and soil physicochemical properties have been conducted. It was found that the increased modularity of the soil bacterial community co-occurrence network and the enrichment of metabolic pathway bacteria had a significant positive effect on the successful regeneration (total effect of 0.84). The complexity of the soil bacterial community gradually decreased with the increase of stand regeneration, and the composition and structure of the flora became simpler (with standard path coefficients: &minus;0.70). In addition, altitude also had a positive effect on regeneration with a total effect of 0.39. Soil nutrients had significantly negative effects on regeneration with total effects of &minus;0.87. Soil bacterial communities may mediate the effects of soil nutrients, altitude, litter thickness, and herbaceous diversity on regeneration in L. principis-rupprechtii plantations. The results provide a great contribution to our understanding of regeneration-soil bacterial community interactions and the basis and important data for sustainable management of L. principis-rupprechtii plantations in the Lvliang Mountains located in northern China

Forest Gaps Modulate the Composition and Co-Occurrence Network of Soil Bacterial Community in <i>Larix principis-rupprechtii</i> Mayr Plantation

Forest gaps create a favorable microenvironment for the growth of the soil microbial community. This study aimed to explore the effects of gap-related microenvironmental heterogeneity on soil bacterial communities in Larix principis-rupprechtii Mayr forest gaps. Therefore, the redundancy analysis (RDA) and structure equations modeling (SEM) of affecting elements were further used to test the significance of forest gaps’ effect on soil bacterial community composition and co-occurrence structure complexity. The formation of forest gaps increased canopy opening (CO) and significantly increased soil moisture content (SW), soil temperature (ST) and the accumulation of acid phosphatase (PHO) and sucrase (INV) in the soil, and the G250 (forest gap size: >250 m2) was most conductive to the accumulation of light and soil total nutrient. G50, G70, and G100 (forest gap size: 50–70 m2, 70–100 m2, 100–125 m2) were most favorable for the natural regeneration of the L. principis-rupprechtii Mayr plantation. The light properties under the forest gaps were the most significant factor that influenced the soil bacterial community composition, followed by the size of the forest gap, with standard path coefficients (Std. PCs) of 0.45 and −0.37, respectively. The canopy opening (CO), relative light intensity (RLA) and leaf area index (LAI) were considered to be the most important environmental factors affecting bacterial community composition (Std. PCs: 0.97, 0.99, and −0.93, respectively). The natural regeneration density under the forest gap was the most significant factor influencing the complexity of the soil bacterial community co-occurrence network, followed by soil nutrients (Std. PCs: 0.87 and −0.76, respectively)

Contrasting Altitudinal Patterns and Composition of Soil Bacterial Communities along Stand Types in <i>Larix principis-rupprechtii</i> Forests in Northern China

Bacterial communities inhabiting the soil of mountain ecosystems perform critical ecological functions. Although several studies have reported the altitudinal distribution patterns of bacterial communities in warm-temperate mountain forests, our understanding of typical zonal vegetation dominated by Larix principis-rupprechtii Mayr (abbreviated as larch hereafter) and the understory elevation distribution patterns of soil bacterial communities is still limited. In this study, the Illumina NovaSeq 6000 sequencing platform was used to investigate the changes of surface and subsurface soil bacterial communities along an altitudinal gradient (from 1720 m to 2250 m) in larch forests in northern China. Altitude significantly affected the relative abundance of Proteobacteria, Actinobacteria, Acidobacteria, and Chloroflexi (bacterial dominant phylum) and Alphaproteobacteria, Gammaproteobacteria, and Actinobacteria (bacterial dominant classes). The diversity of bacterial communities showed a concomitant increase with altitude. The variations in available nitrogen and soil temperature content at different altitudes were the main factors explaining the bacterial community structures in pure stands and mixed stands, respectively. Altitude and the contents of soil organic carbon and soil organic matter were the main factors explaining the dominant phylum (taxonomy). Our results suggest that stand type has a greater effect on the structure and composition of soil bacterial communities than elevation and soil depth, and bacterial communities show divergent patterns along the altitudes, stand types, and soil profiles