Multivariate statistical analysis diagram. by Ying Zhang (40767)

“…<p><b>(a, b)</b> PLS-DA diagrams. The separation pattern was satisfactory, with significant differences in principal components among the groups, indicating that the metabolic profiles underwent significant alterations between different groups. …”

Table 1_Comparative transcriptomic and molecular biology analyses to explore potential immune responses to Vibrio parahaemolyticus challenge in Eriocheir sinensis.docx by Duanduan Chen (6860495)

“…<p>Vibrio parahaemolyticus is a significant pathogen affecting shrimp and crab farming, particularly strains carrying genes associated with acute hepatopancreatic necrosis syndrome. …”

Image 1_EEG-based characterization of auditory attention and meditation: an ERP and machine learning approach.png by Eyad Talal Attar (22124251)

“…A Random Forest classifier reached 86. The system achieved a 7% accuracy rate in differentiating cognitive from meditative states while identifying P300 amplitude and frontal alpha power, together with beta power as significant predictors.…”

Image 8_EEG-based characterization of auditory attention and meditation: an ERP and machine learning approach.png by Eyad Talal Attar (22124251)

“…A Random Forest classifier reached 86. The system achieved a 7% accuracy rate in differentiating cognitive from meditative states while identifying P300 amplitude and frontal alpha power, together with beta power as significant predictors.…”

Data Sheet 1_Visual attention during non-immersive virtual reality balance training in older adults with mild to moderate cognitive impairment: an eye-tracking study.pdf by Marcos Maldonado-Díaz (11221102)

“…Clinical tests revealed a slight increase, though results were not statistically significant in balance and walking speed (p = 0.063). …”

Image 6_EEG-based characterization of auditory attention and meditation: an ERP and machine learning approach.png by Eyad Talal Attar (22124251)

“…A Random Forest classifier reached 86. The system achieved a 7% accuracy rate in differentiating cognitive from meditative states while identifying P300 amplitude and frontal alpha power, together with beta power as significant predictors.…”

Image 2_EEG-based characterization of auditory attention and meditation: an ERP and machine learning approach.png by Eyad Talal Attar (22124251)

“…A Random Forest classifier reached 86. The system achieved a 7% accuracy rate in differentiating cognitive from meditative states while identifying P300 amplitude and frontal alpha power, together with beta power as significant predictors.…”

Image 7_EEG-based characterization of auditory attention and meditation: an ERP and machine learning approach.png by Eyad Talal Attar (22124251)

“…A Random Forest classifier reached 86. The system achieved a 7% accuracy rate in differentiating cognitive from meditative states while identifying P300 amplitude and frontal alpha power, together with beta power as significant predictors.…”

Image 5_EEG-based characterization of auditory attention and meditation: an ERP and machine learning approach.png by Eyad Talal Attar (22124251)

“…A Random Forest classifier reached 86. The system achieved a 7% accuracy rate in differentiating cognitive from meditative states while identifying P300 amplitude and frontal alpha power, together with beta power as significant predictors.…”

Image 4_EEG-based characterization of auditory attention and meditation: an ERP and machine learning approach.png by Eyad Talal Attar (22124251)

“…A Random Forest classifier reached 86. The system achieved a 7% accuracy rate in differentiating cognitive from meditative states while identifying P300 amplitude and frontal alpha power, together with beta power as significant predictors.…”

Image 3_EEG-based characterization of auditory attention and meditation: an ERP and machine learning approach.png by Eyad Talal Attar (22124251)

“…A Random Forest classifier reached 86. The system achieved a 7% accuracy rate in differentiating cognitive from meditative states while identifying P300 amplitude and frontal alpha power, together with beta power as significant predictors.…”

Data Sheet 1_Global soil moisture dynamics: attribution of contributions and their association with GPP.docx by Yang Li (7082)

“…This study further integrates the MODIS MCD12C1 and GOSIF GPP datasets to explore the correlation between SM and GPP on a global scale. The results indicate that between 2000 and 2024, global Total Soil Moisture (TSM) shows a marked declining trend, with SM decreasing synchronously across all seasons. …”

Data Sheet 1_The longitudinal associations between filial piety belief and mobile phone addiction: from the perspective of self-determination theory.zip by Hua Wei (60877)

“…The results showed that reciprocal filial piety belief has a negative impact on mobile phone addiction, while authoritarian filial piety belief has no significant effect on mobile phone addiction. …”

Table 1_Multi-omic analysis of PBMCs in sepsis reveals widespread cytotoxic dysfunction and an increased population of CD69 expressing naïve CD4+ T cells.xlsx by Joshua Flynn (2544523)

“…</p>Methods<p>This study utilised a targeted single-cell multi-omics approach to characterise peripheral blood mononuclear cell (PBMC) populations and their transcriptomic profiles in an Irish cohort of people with (i) sepsis and (ii) bacteraemia without sepsis (defined as clinically significant positive blood culture without sepsis as assessed by the Clinical Microbiology service).…”

Data Sheet 2_Multi-omic analysis of PBMCs in sepsis reveals widespread cytotoxic dysfunction and an increased population of CD69 expressing naïve CD4+ T cells.docx by Joshua Flynn (2544523)

“…</p>Methods<p>This study utilised a targeted single-cell multi-omics approach to characterise peripheral blood mononuclear cell (PBMC) populations and their transcriptomic profiles in an Irish cohort of people with (i) sepsis and (ii) bacteraemia without sepsis (defined as clinically significant positive blood culture without sepsis as assessed by the Clinical Microbiology service).…”

Data Sheet 1_Multi-omic analysis of PBMCs in sepsis reveals widespread cytotoxic dysfunction and an increased population of CD69 expressing naïve CD4+ T cells.docx by Joshua Flynn (2544523)

“…</p>Methods<p>This study utilised a targeted single-cell multi-omics approach to characterise peripheral blood mononuclear cell (PBMC) populations and their transcriptomic profiles in an Irish cohort of people with (i) sepsis and (ii) bacteraemia without sepsis (defined as clinically significant positive blood culture without sepsis as assessed by the Clinical Microbiology service).…”

Land Use Change Transfer Diagram. by Wanzhuang Huang (17357306)

“…The impact of road network density and river network density on the evolution of production-living-ecological land conflicts in the Shiyang River Basin was the most significant. The optimization results of national spatial patterns and regulation strategies could provide a scientific basis for the optimization and comprehensive management of national spatial planning in arid inland river basins.…”

Research framework. by Wanzhuang Huang (17357306)

“…The impact of road network density and river network density on the evolution of production-living-ecological land conflicts in the Shiyang River Basin was the most significant. The optimization results of national spatial patterns and regulation strategies could provide a scientific basis for the optimization and comprehensive management of national spatial planning in arid inland river basins.…”

Image 2_Endocrine-disrupting effects of environmental BPS and PFOS on human brain organoid development.tiff by Andrea Di Credico (13137135)

“…PFOS exposure coincided with decreased transthyretin expression, suggesting a potential influence on thyroid hormone availability.…”

The framework of this study. by Yin Yu (3139023)

“…The results show that (1) the landscape pattern changed visibly from 2000 to 2020, the comprehensive land-use dynamics increased from 8.53% to 9.66%, and the land use dynamics are greatest and change most dramatically on construction sites; (2) the landscape ecological risk level of the study area as a whole showed a declining trend, and 96.82% of the ecological risk plot index decreased, and the risk distribution was dominated by lower risk, medium risk and higher risk areas; (3) landscape ecological risk was spatially positively correlated across all time periods, and the spatial aggregation was gradually weakened, with the distribution of LH and HL areas dispersed and the distribution of HH and LL areas concentrated. …”