Sedimentary response to the intracontinental orogenic process: insight from the anatomy of a small Mesozoic basin in western Yanshan, northern North China

<p>The intra-continental orogeny and tectonic evolution of the Mesozoic Yanshan fold-thrust belt (YFTB) in the northern North China Craton (NCC) have been strongly debated. Here, we focus on the Shangyi basin, located in the centre of the YFTB. An integrated analysis of sedimentary facies, palaeocurrents, clast compositions, and detrital zircon dating of sediments was adopted to determine the palaeogeography, provenance, basin evolution, and intra-continental orogenic process. The Shangyi basin comprises the well-exposed Early–early Middle Jurassic Xiahuayuan Formation and the Longmen Formation, and the Late Jurassic–Early Cretaceous Tuchengzi Formation. Based on the 18 measured sections, five facies associations – including alluvial fan, fluvial, delta, lacustrine, and eolian facies – have been identified and described in detail. The onset of the Shangyi basin was filled with fluvial, deltaic, and lacustrine deposits controlled by the normal fault bounding the northern basin, corresponding to the pre-orogeny. In the Middle Jurassic, the cobble–boulder conglomerates of alluvial fan, as molasse deposits, were compatible with the syn-orogeny of the Yanshan movement, which played a critical role in northern North China and even East Asia. After the depositional break in the Middle–Late Jurassic, the Shangyi basin, controlled by the normal fault present in the north of the basin, re-subsided and quickly expanded southward with thick sedimentation, which is correlative with the post-orogeny. Combined with A-type granites, metamorphic core complexes, mafic dikes, and rift basins of the Late Jurassic–early Early Cretaceous present in the northern NCC and Mongolia, significant extension was widespread in the northern NCC and even in northeast Asia. Moreover, vertical changes of provenance indicate that the Taihang Mountain and the Inner Mongolia palaeo-uplift (IMPU) present at the west and north of the basin, respectively, experienced uplift twice in the Middle–Late Jurassic and Early Cretaceous, resulting in a regional depositional break.</p

Bacterial diversity index calculated from the DGGE banding patterns (Fig. 1A).

<p>N (negative control, basal diet); P (positive control, diet supplemented with neomycin); L, M, H (diets supplemented with probiotics 0.5×10⁹, 1.0×10⁹ and 2.5×10⁹ CFU/kg feed, respectively);</p><p>*1/D, the reciprocal of Simpson diversity index.</p><p>Bacterial diversity index calculated from the DGGE banding patterns (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0116635#pone.0116635.g001" target="_blank">Fig. 1A</a>).</p

<i>Lactobacillus</i> community of weaned piglets fed with neomycin or <i>E. faecalis</i>.

<p>(A) DGGE profiles of V3 region of the 16S rDNA gene fragments with the primes Lac1 and Lac2-GC. The denaturant gradient range is from 41% to 60%. Lanes N (negative control, basal diet); P (positive control, diet supplemented with neomycin); L, M, H (diets supplemented with probiotics 0.5×10⁹, 1.0×10⁹ and 2.5×10⁹ CFU/kg feed, respectively); (B) UPGMA cluster analysis of Dice similarity indices from DGGE profiles.</p

Dietary <i>Enterococcus faecalis</i> LAB31 Improves Growth Performance, Reduces Diarrhea, and Increases Fecal <i>Lactobacillus</i> Number of Weaned Piglets

<div><p>Lactic acid bacteria (LAB) have been shown to enhance performance of weaned piglets. However, few studies have reported the addition of LAB <i>Enterococcus faecalis</i> as alternatives to growth promoting antibiotics for weaned piglets. This study evaluated the effects of dietary <i>E. faecalis</i> LAB31 on the growth performance, diarrhea incidence, blood parameters, fecal bacterial and <i>Lactobacillus</i> communities in weaned piglets. A total of 360 piglets weaned at 26 ± 2 days of age were randomly allotted to 5 groups (20 pens, with 4 pens for each group) for a trial of 28 days: group N (negative control, without antibiotics or probiotics); group P (Neomycin sulfate, 100 mg/kg feed); groups L, M and H (supplemented with <i>E. faecalis</i> LAB31 0.5×10⁹, 1.0×10⁹, and 2.5×10⁹ CFU/kg feed, respectively). Average daily gain and feed conversion efficiency were found to be higher in group H than in group N, and showed significant differences between group H and group P (<i>P₀</i> < 0.05). Furthermore, groups H and P had a lower diarrhea index than the other three groups (<i>P₀</i> < 0.05). Denaturing gradient gel electrophoresis (DGGE) showed that the application of probiotics to the diet changed the bacterial community, with a higher bacterial diversity in group M than in the other four groups. Real-time PCR revealed that the relative number of <i>Lactobacillus</i> increased by addition of probiotics, and was higher in group H than in group N (<i>P₀</i> < 0.05). However, group-specific PCR-DGGE showed no obvious difference among the five groups in <i>Lactobacillus</i> composition and diversity. Therefore, the dietary addition of <i>E. faecalis</i> LAB31 can improve growth performance, reduce diarrhea, and increase the relative number of <i>Lactobacillus</i> in feces of weaned piglets.</p></div

<i>Lactobacillus</i> diversity index calculated from the DGGE banding patterns (Fig. 2A).

<p>N (negative control, basal diet); P (positive control, diet supplemented with neomycin); L, M, H (diets supplemented with probiotics 0.5×10⁹, 1.0×10⁹ and 2.5×10⁹ CFU/kg feed, respectively);</p><p>*1/D, the reciprocal of Simpson diversity index.</p><p><i>Lactobacillus</i> diversity index calculated from the DGGE banding patterns (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0116635#pone.0116635.g002" target="_blank">Fig. 2A</a>).</p

Identification of band fragments in DGGE gels (Fig. 1A).

<p>* Bands are numbered according to <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0116635#pone.0116635.g001" target="_blank">Fig. 1A</a>.</p><p>^◆Identity represents the sequence identity (%) compared with that in the GenBank database.</p><p>Identification of band fragments in DGGE gels (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0116635#pone.0116635.g001" target="_blank">Fig. 1A</a>).</p

Bacterial community of weaned piglets fed with neomycin or <i>E. faecalis</i>.

<p>(A) DGGE profiles of the V6~V8 regions of the 16S rDNA gene fragments from the samples. The denaturant gradient range is from 42% to 58%. The major difference bands are numbered. Lane S (Standard ladder, which are PCR products generated from different bacterial 16S rDNA genes with primers 968F-GC and 1401R); N (negative control, basal diet); P (positive control, diet supplemented with neomycin); L, M, H (diets supplemented with probiotics 0.5×10⁹, 1.0×10⁹ and 2.5×10⁹ CFU/kg feed, respectively); (B) UPGMA cluster analysis of Dice similarity indices from DGGE profiles.</p

Data_Sheet_1_Optimizing the scale-up production of fermented astragalus and its benefits to the performance and egg quality of laying hens.docx

Astragalus is a homologous medicine and food that benefits human beings and poultry rearing. Fermented astragalus (FA) is a valuable product obtained by fermentation, but its scale-up production requires optimization and expansion of solid-state fermentation (SSF). In this study, Lactobacillus pentosus Stm was screened as the most suitable LAB strain for fermenting astragalus due to its excellent capacity. After optimization and expansion of SSF, LAB count and lactic acid content reached 206 × 108 cfu/g and 15.0%, respectively. Meanwhile, the content of bioactive compounds in FA was significantly enhanced. Feeding experiments with laying hens indicated that supplementing FA in the diet significantly improved the performance and egg quality, as evidenced by reduced feed-to-egg ratio and egg cholesterol. This was due to the promotion of intestinal health by shifting intestinal microbiota. Therefore, this is a systematical endeavor of producing scaled-up FA with promising potential as a feed additive in the poultry breeding industry.</p

DataSheet_1_Immune and non-immune cell subtypes identify novel targets for prognostic and therapeutic strategy: A study based on intratumoral heterogenicity analysis of multicenter scRNA-seq datasets in lung adenocarcinoma.pdf

Lung adenocarcinoma (LUAD) is the most common type of lung cancer and the leading cause of cancer incidence and mortality worldwide. Despite the improvement of traditional and immunological therapies, the clinical outcome of LUAD is still far from satisfactory. Patients given the same treatment regimen had different responses and clinical outcomes due to the heterogeneity of LUAD. How to identify the targets based on heterogeneity analysis is crucial for treatment strategies. Recently, the single-cell RNA-sequencing (scRNA-seq) technology has been used to investigate the tumor microenvironment (TME) based on cell-specific changes and shows prominently valuable for biomarker prediction. In this study, we systematically analyzed a meta-dataset from the multiple LUAD scRNA-seq datasets in LUAD, identified 15 main types of cells and 57 cell subgroups, and revealed a series of potential biomarkers in M2b, exhausted CD8+T, endothelial cells, fibroblast, and metabolic patterns in TME, which further validated with immunofluorescence in clinical cohorts of LUAD. In the prognosis analysis, M0 macrophage and T cell activation were shown correlated to a better prognosis (p<0.05). Briefly, our study provided insights into the heterogeneity of LUAD and assisted in novel therapeutic strategies for clinical outcome improvement.</p

Table_1_Immune and non-immune cell subtypes identify novel targets for prognostic and therapeutic strategy: A study based on intratumoral heterogenicity analysis of multicenter scRNA-seq datasets in lung adenocarcinoma.xlsx

Lung adenocarcinoma (LUAD) is the most common type of lung cancer and the leading cause of cancer incidence and mortality worldwide. Despite the improvement of traditional and immunological therapies, the clinical outcome of LUAD is still far from satisfactory. Patients given the same treatment regimen had different responses and clinical outcomes due to the heterogeneity of LUAD. How to identify the targets based on heterogeneity analysis is crucial for treatment strategies. Recently, the single-cell RNA-sequencing (scRNA-seq) technology has been used to investigate the tumor microenvironment (TME) based on cell-specific changes and shows prominently valuable for biomarker prediction. In this study, we systematically analyzed a meta-dataset from the multiple LUAD scRNA-seq datasets in LUAD, identified 15 main types of cells and 57 cell subgroups, and revealed a series of potential biomarkers in M2b, exhausted CD8+T, endothelial cells, fibroblast, and metabolic patterns in TME, which further validated with immunofluorescence in clinical cohorts of LUAD. In the prognosis analysis, M0 macrophage and T cell activation were shown correlated to a better prognosis (p<0.05). Briefly, our study provided insights into the heterogeneity of LUAD and assisted in novel therapeutic strategies for clinical outcome improvement.</p