Crude rates in select US counties between 1999 and 2024. by Tony Wong (779773)

R script used for modelling analyses. by Roland Proud (11764892)

Snail count data. by Roland Proud (11764892)

Human parasitology infection intensity data. by Roland Proud (11764892)

Human parasitology prevalence data. by Roland Proud (11764892)

Summary of snail survey effort and count data. Occupancy and counts are given as area averages across site means, with associated site mean ranges given in brackets. Note that both... by Roland Proud (11764892)

Flow diagram of participants selection. by Wei Huang (36889)

“…</p><p>Results</p><p>Each 1-unit BRI increase elevated LMM risk by 73% (OR=1.73, 95%CI = 1.61–1.86, <i><i>p</i></i> < 0.0001). …”

Minimal data set. by Wei Huang (36889)

“…</p><p>Results</p><p>Each 1-unit BRI increase elevated LMM risk by 73% (OR=1.73, 95%CI = 1.61–1.86, <i><i>p</i></i> < 0.0001). …”

Weighted comparison of baseline characteristics. by Wei Huang (36889)

“…</p><p>Results</p><p>Each 1-unit BRI increase elevated LMM risk by 73% (OR=1.73, 95%CI = 1.61–1.86, <i><i>p</i></i> < 0.0001). …”

S1 File - by Pengyu Yang (2668450)

“…The findings reveal that: (1) At this stage, digital transformation in listed companies effectively reduces their carbon intensity, but the relationship between the two is not linear; instead, it exhibits a U-shaped trajectory, initially decreasing then increasing. (2) Analysis of mechanism indicates that costs associated with environmental governance and innovations in green technology serve as critical pathways through which corporate digital transformation influences carbon intensity. (3) The analysis of driving effect suggests that the digital transformation significantly curtails the carbon emission intensity of both upstream and downstream enterprises as well as those within the same industry and geographical region, through industrial linkage and the cohort effect. (4) Heterogeneity analysis elucidates that the digital transformation of enterprises in regions with stronger government environmental regulations has a markedly more pronounced effect on reducing the carbon emission intensity. …”

Variables influencing the risk of consultation (details available in by Luther Dogbla (21410142)

Summary of KEGG enrichment analysis. by Zeynab Hosseinpoor (19762319)

“…Based on Real-Time PCR results, the relative expression of the selected genes responded differently to overexpressed <i>MIR-96</i>. While the expression levels of <i>IRS2</i>, <i>FOXO1</i>, and <i>ERK2</i> (<i>MAPK1</i>) were significantly decreased, the <i>SERPINF1</i> gene exhibited high expression simultaneously. pAAV-delivered <i>MIR-96</i> had no adverse effect on the viability of human RPE cells. …”

Gene ontology enrichment analysis. by Zeynab Hosseinpoor (19762319)

Network of differentially expressed genes associated with diabetic retinopathy. by Zeynab Hosseinpoor (19762319)

Baseline characteristics of study subjects. by Md. Akeruzzaman Shaon (21511929)

“…Our study discovered that the rs12529 polymorphism of the <i>AKR1C3</i> gene has an enhanced risk of susceptibility to breast malignancy (p = 0.016, OR = 1.97, 95% CI = 1.22 to 3.16 for the GG genotype in additive model 2). …”

Demographic characteristics of the breast cancer patients enrolled in the study. by Md. Akeruzzaman Shaon (21511929)

Changes in the body color of cherry shrimp (<i>N</i>. <i>davidi</i> var. red) across different feed treatment groups (Control, AX100, AX100 + BP, AX200, AX200 + BP) over time. by Wei-Wei Hou (3405947)

Trends of monthly, seasonal and annual rainfall (mm/year) in Wollo (1981–2022) (shapefile extracted from https://hub.arcgis.com/datasets/esri::world-countries/explore?location=-1.4... by Asnake Adane (19935018)

Proteome and Metabolome Profiling of Anticoagulant Disorders Induced by Familial Protein S Deficiency by Caiping Zhang (6745976)

“…Furthermore, 9 differential proteins correlated significantly with protein S, comprising A2M, AGT, APOE, FGG, GPLD1, IGHV1–69, CFHR5, CPN2, and CA1. …”

Proteome and Metabolome Profiling of Anticoagulant Disorders Induced by Familial Protein S Deficiency by Caiping Zhang (6745976)

“…Furthermore, 9 differential proteins correlated significantly with protein S, comprising A2M, AGT, APOE, FGG, GPLD1, IGHV1–69, CFHR5, CPN2, and CA1. …”