Carbon-Sequestration Straw Cellulose-Aerogel Gradient Thermal Insulation Material

Green superinsulation materials are essential for net-zero sustainable building envelopes. Realizing such potential is indispensable for simultaneously achieving carbon-sequestration and superinsulation performance. Here, we report the synthesis of a water glass-based silica aerogel exhibiting a thermal conductivity of 17.2 mW/m·K and a high porosity of 92%. We used carbon-sequestration wheat straw fiber to create a gradient cellulose-aerogel composite to improve mechanical stability. The as-prepared gradient composite exhibits a thermal conductivity of 27.1 mW/m·K and a flexural modulus of 824 MPa, while exhibiting superhydrophobicity (water contact angle of 135.4°) for the development of green building insulation materials

Table_3_The mutual interactions among Helicobacter pylori, chronic gastritis, and the gut microbiota: a population-based study in Jinjiang, Fujian.XLSX

ObjectivesHelicobacter pylori (H. pylori) is a type of bacteria that infects the stomach lining, and it is a major cause of chronic gastritis (CG). H. pylori infection can influence the composition of the gastric microbiota. Additionally, alterations in the gut microbiome have been associated with various health conditions, including gastrointestinal disorders. The dysbiosis in gut microbiota of human is associated with the decreased secretion of gastric acid. Chronic atrophic gastritis (CAG) and H. pylori infection are also causes of reduced gastric acid secretion. However, the specific details of how H. pylori infection and CG, especially for CAG, influence the gut microbiome can vary and are still an area of ongoing investigation. The incidence of CAG and infection rate of H. pylori has obvious regional characteristics, and Fujian Province in China is a high incidence area of CAG as well as H. pylori infection. We aimed to characterize the microbial changes and find potential diagnostic markers associated with infection of H. pylori as well as CG of subjects in Jinjiang City, Fujian Province, China.ParticipantsEnrollment involved sequencing the 16S rRNA gene in fecal samples from 176 cases, adhering to stringent inclusion and exclusion criteria. For our study, we included healthy volunteers (Normal), individuals with chronic non-atrophic gastritis (CNAG), and those with CAG from Fujian, China. The aim was to assess gut microbiome dysbiosis based on various histopathological features. QIIME and LEfSe analyses were performed. There were 176 cases, comprising 126 individuals who tested negative for H. pylori and 50 who tested positive defined by C14 urea breath tests and histopathological findings in biopsies obtained through endoscopy. CAG was also staged by applying OLGIM system.ResultsWhen merging the outcomes from 16S rRNA gene sequencing results, there were no notable variations in alpha diversity among the following groups: Normal, CNAG, and CAG; OLGIM I and OLGIM II; and H. pylori positive [Hp (+)] and H. pylori negative [Hp (–)] groups. Beta diversity among different groups show significant separation through the NMDS diagrams. LEfSe analyses confirmed 2, 3, and 6 bacterial species were in abundance in the Normal, CNAG, and CAG groups; 26 and 2 species in the OLGIM I and OLGIM II group; 22 significant phylotypes were identified in Hp (+) and Hp (–) group, 21 and 1, respectively; 9 bacterial species exhibited significant differences between individuals with CG who were Hp (+) and those who were Hp (–).ConclusionThe study uncovered notable distinctions in the characteristics of gut microbiota among the following groups: Normal, CNAG, and CAG; OLGIM I and OLGIM II; and Hp (+) and Hp (–) groups. Through the analysis of H. pylori infection in CNAG and CAG groups, we found the gut microbiota characteristics of different group show significant difference because of H. pylori infection. Several bacterial genera could potentially serve as diagnostic markers for H. pylori infection and the progression of CG.</p

Table_2_The mutual interactions among Helicobacter pylori, chronic gastritis, and the gut microbiota: a population-based study in Jinjiang, Fujian.XLSX

ObjectivesHelicobacter pylori (H. pylori) is a type of bacteria that infects the stomach lining, and it is a major cause of chronic gastritis (CG). H. pylori infection can influence the composition of the gastric microbiota. Additionally, alterations in the gut microbiome have been associated with various health conditions, including gastrointestinal disorders. The dysbiosis in gut microbiota of human is associated with the decreased secretion of gastric acid. Chronic atrophic gastritis (CAG) and H. pylori infection are also causes of reduced gastric acid secretion. However, the specific details of how H. pylori infection and CG, especially for CAG, influence the gut microbiome can vary and are still an area of ongoing investigation. The incidence of CAG and infection rate of H. pylori has obvious regional characteristics, and Fujian Province in China is a high incidence area of CAG as well as H. pylori infection. We aimed to characterize the microbial changes and find potential diagnostic markers associated with infection of H. pylori as well as CG of subjects in Jinjiang City, Fujian Province, China.ParticipantsEnrollment involved sequencing the 16S rRNA gene in fecal samples from 176 cases, adhering to stringent inclusion and exclusion criteria. For our study, we included healthy volunteers (Normal), individuals with chronic non-atrophic gastritis (CNAG), and those with CAG from Fujian, China. The aim was to assess gut microbiome dysbiosis based on various histopathological features. QIIME and LEfSe analyses were performed. There were 176 cases, comprising 126 individuals who tested negative for H. pylori and 50 who tested positive defined by C14 urea breath tests and histopathological findings in biopsies obtained through endoscopy. CAG was also staged by applying OLGIM system.ResultsWhen merging the outcomes from 16S rRNA gene sequencing results, there were no notable variations in alpha diversity among the following groups: Normal, CNAG, and CAG; OLGIM I and OLGIM II; and H. pylori positive [Hp (+)] and H. pylori negative [Hp (–)] groups. Beta diversity among different groups show significant separation through the NMDS diagrams. LEfSe analyses confirmed 2, 3, and 6 bacterial species were in abundance in the Normal, CNAG, and CAG groups; 26 and 2 species in the OLGIM I and OLGIM II group; 22 significant phylotypes were identified in Hp (+) and Hp (–) group, 21 and 1, respectively; 9 bacterial species exhibited significant differences between individuals with CG who were Hp (+) and those who were Hp (–).ConclusionThe study uncovered notable distinctions in the characteristics of gut microbiota among the following groups: Normal, CNAG, and CAG; OLGIM I and OLGIM II; and Hp (+) and Hp (–) groups. Through the analysis of H. pylori infection in CNAG and CAG groups, we found the gut microbiota characteristics of different group show significant difference because of H. pylori infection. Several bacterial genera could potentially serve as diagnostic markers for H. pylori infection and the progression of CG.</p

Table_1_The mutual interactions among Helicobacter pylori, chronic gastritis, and the gut microbiota: a population-based study in Jinjiang, Fujian.XLSX

ObjectivesHelicobacter pylori (H. pylori) is a type of bacteria that infects the stomach lining, and it is a major cause of chronic gastritis (CG). H. pylori infection can influence the composition of the gastric microbiota. Additionally, alterations in the gut microbiome have been associated with various health conditions, including gastrointestinal disorders. The dysbiosis in gut microbiota of human is associated with the decreased secretion of gastric acid. Chronic atrophic gastritis (CAG) and H. pylori infection are also causes of reduced gastric acid secretion. However, the specific details of how H. pylori infection and CG, especially for CAG, influence the gut microbiome can vary and are still an area of ongoing investigation. The incidence of CAG and infection rate of H. pylori has obvious regional characteristics, and Fujian Province in China is a high incidence area of CAG as well as H. pylori infection. We aimed to characterize the microbial changes and find potential diagnostic markers associated with infection of H. pylori as well as CG of subjects in Jinjiang City, Fujian Province, China.ParticipantsEnrollment involved sequencing the 16S rRNA gene in fecal samples from 176 cases, adhering to stringent inclusion and exclusion criteria. For our study, we included healthy volunteers (Normal), individuals with chronic non-atrophic gastritis (CNAG), and those with CAG from Fujian, China. The aim was to assess gut microbiome dysbiosis based on various histopathological features. QIIME and LEfSe analyses were performed. There were 176 cases, comprising 126 individuals who tested negative for H. pylori and 50 who tested positive defined by C14 urea breath tests and histopathological findings in biopsies obtained through endoscopy. CAG was also staged by applying OLGIM system.ResultsWhen merging the outcomes from 16S rRNA gene sequencing results, there were no notable variations in alpha diversity among the following groups: Normal, CNAG, and CAG; OLGIM I and OLGIM II; and H. pylori positive [Hp (+)] and H. pylori negative [Hp (–)] groups. Beta diversity among different groups show significant separation through the NMDS diagrams. LEfSe analyses confirmed 2, 3, and 6 bacterial species were in abundance in the Normal, CNAG, and CAG groups; 26 and 2 species in the OLGIM I and OLGIM II group; 22 significant phylotypes were identified in Hp (+) and Hp (–) group, 21 and 1, respectively; 9 bacterial species exhibited significant differences between individuals with CG who were Hp (+) and those who were Hp (–).ConclusionThe study uncovered notable distinctions in the characteristics of gut microbiota among the following groups: Normal, CNAG, and CAG; OLGIM I and OLGIM II; and Hp (+) and Hp (–) groups. Through the analysis of H. pylori infection in CNAG and CAG groups, we found the gut microbiota characteristics of different group show significant difference because of H. pylori infection. Several bacterial genera could potentially serve as diagnostic markers for H. pylori infection and the progression of CG.</p

Expression statistic of different lncRNAs and mRNA.

<p>Expression statistic of different lncRNAs and mRNA.</p

Families prediction of lncRNAs.

<p>Families prediction of lncRNAs.</p

Identification and analysis of differentially expressed long non-coding RNAs between multiparous and uniparous goat (<i>Capra hircus</i>) ovaries

<div><p>Long non-coding RNAs (lncRNAs) play important roles in almost all biological processes. However, there is little information on the effects of lncRNAs on ovulation and lambing rates. In the present study, we used high-throughput RNA sequencing to identify differentially expressed lncRNAs between the ovaries of multiparous (Mul) and uniparous (Uni) Anhui White goats. Among the 107,255,422 clean reads, 183,754 lncRNAs were significantly differentially expressed between the Uni and Mul. Among them, 455 lncRNAs were co-expressed between the two samples, whereas, 157,523 lncRNAs were uniquely expressed in the Uni, and 25,776 uniquely lncRNAs were expressed in the Mul. Through Cis role analysis, 24 lncRNAs were predicted to overlap with cis-regulatory elements, which involved in Progesterone-mediated oocyte maturation, Steroid biosynthesis, Oocyte meiosis, and gonadotropin-releasing hormone (GnRH) signaling pathway. These 4 pathways were related to ovulation, and the KEGG pathway analysis on target genes of the differentially expressed lncRNAs confirmed this results. In addition, 10 lncRNAs harbored precursors of 40 miRNAs, such as TCONS_00320849 related to a mature miRNA sequence, miR-365a, which was reported to be related to proliferation, were annotated in the precursor analysis of miRNAs. The present expand the understanding of lncRNA biology and contribute to the annotation of the goat genome. The study will provide a resource for lncRNA studies of ovulation and lambing.</p></div

Up/down stream lncRNA of a gene.

<p>Up/down stream lncRNA of a gene.</p

Real-time PCR results of randomly selected differentially expressed lncRNAs.

<p>Note: X-axis represents selected 8 differential expressed transcripts in two libraries. Here, GAPDH was chosen as the reference gene. Relative expression value per selected transcripts between uniparous goats (Uni) and multiparous goats (Mul) samples was calculated (y-axis). Superscript letters indicate significant difference at the level of 0.05.</p

Transcript coverage stat of uniparous goats (Uni) and multiparou goats (Mul).

<p>Transcript coverage stat of uniparous goats (Uni) and multiparou goats (Mul).</p