Image 3_Isolation and identification of Alternaria alstroemeriae causing postharvest black rot in citrus and its control using curcumin-loaded nanoliposomes.jpeg by Jie Li (15030)

“…Curcumin-loaded nanoliposomes also enhanced the activities of the enzymes PPO, APX, POD, PAL, GR and CAT of citrus, decreased the O₂⁻ production rate. …”

Comparison of 3D culture systems on mesenchymal stem cell longevity, regenerative potential, and secretome production by Jacob Hodge (2699521)

“…<p>Mesenchymal stem/stromal cells (MSCs) are widely investigated for regenerative therapies, yet current expansion methods often compromise their stem-like properties, limiting large-scale clinical translation. We aimed to evaluate whether a novel hydrogel-based Bio-Block® platform preserved intrinsic adipose-derived MSC (ASCs) phenotype and secretome compared with conventional systems.…”

Image 10_Isolation and identification of Alternaria alstroemeriae causing postharvest black rot in citrus and its control using curcumin-loaded nanoliposomes.jpeg by Jie Li (15030)

“…Curcumin-loaded nanoliposomes also enhanced the activities of the enzymes PPO, APX, POD, PAL, GR and CAT of citrus, decreased the O₂⁻ production rate. …”

Table2_Causal relationship between plasma lipidome and four types of pancreatitis: a bidirectional Mendelian randomization study.xlsx by Runzhou Ma (19758996)

“…</p>Methods<p>Therefore, our study aimed to investigate the causal relationships between plasma lipidome and four types of pancreatitis by conducting a bidirectional, two-sample Mendelian randomization (MR) analysis. We obtained genetic variants associated with 179 lipid species from a Genome-wide association analysis of plasma lipidome. …”

Table4_Causal relationship between plasma lipidome and four types of pancreatitis: a bidirectional Mendelian randomization study.xlsx by Runzhou Ma (19758996)

“…</p>Methods<p>Therefore, our study aimed to investigate the causal relationships between plasma lipidome and four types of pancreatitis by conducting a bidirectional, two-sample Mendelian randomization (MR) analysis. We obtained genetic variants associated with 179 lipid species from a Genome-wide association analysis of plasma lipidome. …”

Table1_Causal relationship between plasma lipidome and four types of pancreatitis: a bidirectional Mendelian randomization study.xlsx by Runzhou Ma (19758996)

“…</p>Methods<p>Therefore, our study aimed to investigate the causal relationships between plasma lipidome and four types of pancreatitis by conducting a bidirectional, two-sample Mendelian randomization (MR) analysis. We obtained genetic variants associated with 179 lipid species from a Genome-wide association analysis of plasma lipidome. …”

DataSheet1_Causal relationship between plasma lipidome and four types of pancreatitis: a bidirectional Mendelian randomization study.pdf by Runzhou Ma (19758996)

“…</p>Methods<p>Therefore, our study aimed to investigate the causal relationships between plasma lipidome and four types of pancreatitis by conducting a bidirectional, two-sample Mendelian randomization (MR) analysis. We obtained genetic variants associated with 179 lipid species from a Genome-wide association analysis of plasma lipidome. …”

Table5_Causal relationship between plasma lipidome and four types of pancreatitis: a bidirectional Mendelian randomization study.xlsx by Runzhou Ma (19758996)

“…</p>Methods<p>Therefore, our study aimed to investigate the causal relationships between plasma lipidome and four types of pancreatitis by conducting a bidirectional, two-sample Mendelian randomization (MR) analysis. We obtained genetic variants associated with 179 lipid species from a Genome-wide association analysis of plasma lipidome. …”

Table3_Causal relationship between plasma lipidome and four types of pancreatitis: a bidirectional Mendelian randomization study.xlsx by Runzhou Ma (19758996)

“…</p>Methods<p>Therefore, our study aimed to investigate the causal relationships between plasma lipidome and four types of pancreatitis by conducting a bidirectional, two-sample Mendelian randomization (MR) analysis. We obtained genetic variants associated with 179 lipid species from a Genome-wide association analysis of plasma lipidome. …”

Land use intensity classes standard. by Chao Ma (207385)

“…The habitat quality shows a spatial distribution pattern of “high in the surrounding areas and low in the central areas”, and autocorrelation analysis shows that county-level units have significant spatial agglomeration effects. (iii) The overall type shows an enhancement of dual factor or non-linear, in which land use intensity and population density are the main driving factors for the spatio-temporal evolution of habitat quality. …”

Land use transfer matrix 1990-2020 (km²). by Chao Ma (207385)

“…The habitat quality shows a spatial distribution pattern of “high in the surrounding areas and low in the central areas”, and autocorrelation analysis shows that county-level units have significant spatial agglomeration effects. (iii) The overall type shows an enhancement of dual factor or non-linear, in which land use intensity and population density are the main driving factors for the spatio-temporal evolution of habitat quality. …”

Study area habitat quality LISA clustering map. by Chao Ma (207385)

“…The habitat quality shows a spatial distribution pattern of “high in the surrounding areas and low in the central areas”, and autocorrelation analysis shows that county-level units have significant spatial agglomeration effects. (iii) The overall type shows an enhancement of dual factor or non-linear, in which land use intensity and population density are the main driving factors for the spatio-temporal evolution of habitat quality. …”

Spato-temporal changes in land use types. by Chao Ma (207385)

“…The habitat quality shows a spatial distribution pattern of “high in the surrounding areas and low in the central areas”, and autocorrelation analysis shows that county-level units have significant spatial agglomeration effects. (iii) The overall type shows an enhancement of dual factor or non-linear, in which land use intensity and population density are the main driving factors for the spatio-temporal evolution of habitat quality. …”

Pattern indices of landscape levels. by Chao Ma (207385)

“…The habitat quality shows a spatial distribution pattern of “high in the surrounding areas and low in the central areas”, and autocorrelation analysis shows that county-level units have significant spatial agglomeration effects. (iii) The overall type shows an enhancement of dual factor or non-linear, in which land use intensity and population density are the main driving factors for the spatio-temporal evolution of habitat quality. …”

Type level landscape index changes in 1990-2020. by Chao Ma (207385)

“…The habitat quality shows a spatial distribution pattern of “high in the surrounding areas and low in the central areas”, and autocorrelation analysis shows that county-level units have significant spatial agglomeration effects. (iii) The overall type shows an enhancement of dual factor or non-linear, in which land use intensity and population density are the main driving factors for the spatio-temporal evolution of habitat quality. …”

Location map of the study area. by Chao Ma (207385)

“…The habitat quality shows a spatial distribution pattern of “high in the surrounding areas and low in the central areas”, and autocorrelation analysis shows that county-level units have significant spatial agglomeration effects. (iii) The overall type shows an enhancement of dual factor or non-linear, in which land use intensity and population density are the main driving factors for the spatio-temporal evolution of habitat quality. …”

Data source. by Chao Ma (207385)

“…The habitat quality shows a spatial distribution pattern of “high in the surrounding areas and low in the central areas”, and autocorrelation analysis shows that county-level units have significant spatial agglomeration effects. (iii) The overall type shows an enhancement of dual factor or non-linear, in which land use intensity and population density are the main driving factors for the spatio-temporal evolution of habitat quality. …”

Research Technology Flow Chart. by Chao Ma (207385)

“…The habitat quality shows a spatial distribution pattern of “high in the surrounding areas and low in the central areas”, and autocorrelation analysis shows that county-level units have significant spatial agglomeration effects. (iii) The overall type shows an enhancement of dual factor or non-linear, in which land use intensity and population density are the main driving factors for the spatio-temporal evolution of habitat quality. …”

Supplementary file 1_Effects of nitrogen addition on SOC in alpine grasslands of the Qinghai-Tibetan Plateau and adjacent mountain regions: a meta-analysis.docx by Yanhui Ye (22775894)

“…The addition of nitrogen decreased the soil pH by 0.20 units and the soil C/N ratio by 1.7%. …”

3D culture raw data by Yaojun Luo (20296512)

“…Notably, N⁶-methyladenosine (m⁶A), the predominant internal modification on mRNAs in eukaryotes, was significantly decreased in 3D cultured eWAT preadipocytes at day 1 post-treatment with the MDI cocktail. …”