BioSCape Processed Training Dataset by Alanna Rebelo (17834777)

“…The dataset includes training data points of invasive alien trees and other classes for the three study sites in Cape Floristic Region (<b>Figure </b><b>1</b>). The dataset was prepared according to the following pre-agreed criteria:</p><ul><li>As many points as possible were collected</li><li>The classes needed to be even (same number of training points) for the machine learning algorithms</li><li>Points didn’t need to be paired (i.e. paired invasive alien tree and fynbos points)</li><li>It was not necessary to collect training data in all sampling units, though a general effort to avoid bias and to sample across different sampling units was attempted</li></ul><p></p>…”

Table 5_MMPred: a tool to predict peptide mimicry events in MHC class II recognition.xlsx by Filippo Guerri (17017524)

“…To evaluate MMPred’s ability to produce biologically meaningful insights, we conducted a comprehensive assessment involving i) predicting associations between known HLA class II human autoepitopes and microbial-peptide mimicry, ii) interpreting these predictions within a systems biology framework to identify potential functional links between the predicted autoantigens and pathophysiological pathways related to autoimmune diseases, and iii) analyzing illustrative cases in the context of SARS-CoV-2 infection and autoimmunity. …”

Table 3_MMPred: a tool to predict peptide mimicry events in MHC class II recognition.xlsx by Filippo Guerri (17017524)

“…To evaluate MMPred’s ability to produce biologically meaningful insights, we conducted a comprehensive assessment involving i) predicting associations between known HLA class II human autoepitopes and microbial-peptide mimicry, ii) interpreting these predictions within a systems biology framework to identify potential functional links between the predicted autoantigens and pathophysiological pathways related to autoimmune diseases, and iii) analyzing illustrative cases in the context of SARS-CoV-2 infection and autoimmunity. …”

Table 6_MMPred: a tool to predict peptide mimicry events in MHC class II recognition.xlsx by Filippo Guerri (17017524)

“…To evaluate MMPred’s ability to produce biologically meaningful insights, we conducted a comprehensive assessment involving i) predicting associations between known HLA class II human autoepitopes and microbial-peptide mimicry, ii) interpreting these predictions within a systems biology framework to identify potential functional links between the predicted autoantigens and pathophysiological pathways related to autoimmune diseases, and iii) analyzing illustrative cases in the context of SARS-CoV-2 infection and autoimmunity. …”

Table 2_MMPred: a tool to predict peptide mimicry events in MHC class II recognition.xlsx by Filippo Guerri (17017524)

“…To evaluate MMPred’s ability to produce biologically meaningful insights, we conducted a comprehensive assessment involving i) predicting associations between known HLA class II human autoepitopes and microbial-peptide mimicry, ii) interpreting these predictions within a systems biology framework to identify potential functional links between the predicted autoantigens and pathophysiological pathways related to autoimmune diseases, and iii) analyzing illustrative cases in the context of SARS-CoV-2 infection and autoimmunity. …”

Table 4_MMPred: a tool to predict peptide mimicry events in MHC class II recognition.xlsx by Filippo Guerri (17017524)

“…To evaluate MMPred’s ability to produce biologically meaningful insights, we conducted a comprehensive assessment involving i) predicting associations between known HLA class II human autoepitopes and microbial-peptide mimicry, ii) interpreting these predictions within a systems biology framework to identify potential functional links between the predicted autoantigens and pathophysiological pathways related to autoimmune diseases, and iii) analyzing illustrative cases in the context of SARS-CoV-2 infection and autoimmunity. …”

Table 7_MMPred: a tool to predict peptide mimicry events in MHC class II recognition.xlsx by Filippo Guerri (17017524)

“…To evaluate MMPred’s ability to produce biologically meaningful insights, we conducted a comprehensive assessment involving i) predicting associations between known HLA class II human autoepitopes and microbial-peptide mimicry, ii) interpreting these predictions within a systems biology framework to identify potential functional links between the predicted autoantigens and pathophysiological pathways related to autoimmune diseases, and iii) analyzing illustrative cases in the context of SARS-CoV-2 infection and autoimmunity. …”

Table 1_MMPred: a tool to predict peptide mimicry events in MHC class II recognition.xlsx by Filippo Guerri (17017524)

“…To evaluate MMPred’s ability to produce biologically meaningful insights, we conducted a comprehensive assessment involving i) predicting associations between known HLA class II human autoepitopes and microbial-peptide mimicry, ii) interpreting these predictions within a systems biology framework to identify potential functional links between the predicted autoantigens and pathophysiological pathways related to autoimmune diseases, and iii) analyzing illustrative cases in the context of SARS-CoV-2 infection and autoimmunity. …”

Table 8_MMPred: a tool to predict peptide mimicry events in MHC class II recognition.xlsx by Filippo Guerri (17017524)

“…To evaluate MMPred’s ability to produce biologically meaningful insights, we conducted a comprehensive assessment involving i) predicting associations between known HLA class II human autoepitopes and microbial-peptide mimicry, ii) interpreting these predictions within a systems biology framework to identify potential functional links between the predicted autoantigens and pathophysiological pathways related to autoimmune diseases, and iii) analyzing illustrative cases in the context of SARS-CoV-2 infection and autoimmunity. …”

Cringe - Emotional manipulation as a tool of political control in Albanian digital campaigns (2025) by Edlira Gugu (21793610)

“…Through mixed-methods analysis of 3,247 social media posts, 1,856 videos, and 2,156 memes, this study documents a radical transformation in political discourse where traditional policy-based communication (27%) was overwhelmed by cringe-based content (73%).</p><p dir="ltr"><b>Key findings include:</b></p><ul><li>Perfect correlation (r = 1.000) between cringe intensity and electoral success</li><li>Identification of six strategic cringe tactics used by politicians</li><li>Discovery of 58 anonymous pages using artificial amplification (ratios up to 396:1)</li><li>Evidence of cringe functioning as affective governance and social control</li></ul><p dir="ltr"><b>Dataset contents:</b></p><ul><li>Full research paper (English & Albanian versions)</li><li>Interactive data visualizations and statistical analysis</li><li>Comprehensive emotions lexicon for cringe phenomenon</li><li>Detailed supplementary materials with coded examples</li><li>Methodology documentation and ethics compliance records</li></ul><p dir="ltr"><b>Methodology:</b> Mixed-methods approach combining computational analysis with qualitative discourse analysis. …”

A paired dataset of multi-modal MRI at 3 Tesla and 7 Tesla with manual hippocampal subfield segmentations on 7T T2-weighted images by Shuyu Li (18859198)

“…<p dir="ltr">The hippocampus, a region of critical interest within clinical neuroscience, is recognized as a complex structure comprising distinct subfields with unique functional attributes, connectivity patterns, and susceptibilities to disease. …”

AP-2α 相关研究 by Ya-Hong Wang (21080642)

“…**P < 0.01 (Student’s t-test).</p><p dir="ltr"><b>Figure 4 | Comparative transcriptomic analysis of the functional regulation of </b><b><i>VdAP-2α</i></b><b> in </b><b><i>Verticillium dahliae.…”

Data Sheet 1_Deep learning in microbiome analysis: a comprehensive review of neural network models.pdf by Piotr Przymus (10165658)

“…These computational techniques have become essential for addressing the inherent complexity and high-dimensionality of microbiome data, which consist of different types of omics datasets. Deep learning algorithms have shown remarkable capabilities in pattern recognition, feature extraction, and predictive modeling, enabling researchers to uncover hidden relationships within microbial ecosystems. …”

Data Sheet 2_Deep learning in microbiome analysis: a comprehensive review of neural network models.pdf by Piotr Przymus (10165658)

“…These computational techniques have become essential for addressing the inherent complexity and high-dimensionality of microbiome data, which consist of different types of omics datasets. Deep learning algorithms have shown remarkable capabilities in pattern recognition, feature extraction, and predictive modeling, enabling researchers to uncover hidden relationships within microbial ecosystems. …”

Leadership at the Borders: Reimagining Organizational Development Amid Disrupted Context by Nawaf Al-Ghanem (17925863)

“…Digital infrastructures expand the capacity for cross-border collaboration yet expose organizations to vulnerabilities in cybersecurity, data ethics, and algorithmic inequality. Meanwhile, leadership within multicultural and transnational environments requires navigation across cultural and normative frontiers, demanding a shift from control to coordination, from authority to adaptability, and from hierarchy to networked influence.…”

Image 2_Spatial transcriptomic analysis of 4NQO-induced tongue cancer revealed cellular lineage diversity and evolutionary trajectory.tif by Feng Liu (72874)

“…</p>Methods<p>We leveraged artificial intelligence (AI) algorithms and spatial transcriptomic sequencing to meticulously characterize the spatial and temporal evolution of 4-nitroquinoline-1-oxide (4NQO)-induced tongue carcinogenesis and intratumor heterogeneity.…”

Image 1_Spatial transcriptomic analysis of 4NQO-induced tongue cancer revealed cellular lineage diversity and evolutionary trajectory.tif by Feng Liu (72874)

“…</p>Methods<p>We leveraged artificial intelligence (AI) algorithms and spatial transcriptomic sequencing to meticulously characterize the spatial and temporal evolution of 4-nitroquinoline-1-oxide (4NQO)-induced tongue carcinogenesis and intratumor heterogeneity.…”

Image 3_Spatial transcriptomic analysis of 4NQO-induced tongue cancer revealed cellular lineage diversity and evolutionary trajectory.tif by Feng Liu (72874)

“…</p>Methods<p>We leveraged artificial intelligence (AI) algorithms and spatial transcriptomic sequencing to meticulously characterize the spatial and temporal evolution of 4-nitroquinoline-1-oxide (4NQO)-induced tongue carcinogenesis and intratumor heterogeneity.…”

Data Sheet 2_Integrated analysis of single-cell RNA-seq and spatial transcriptomics to identify the lactylation-related protein TUBB2A as a potential biomarker for glioblastoma in... by Yifan Xu (87249)

“…</p>Methods<p>We employed functional enrichment algorithms, including AUCell and UCell, to assess lactylation activity in GBM cancer cells. …”

Data Sheet 9_Integrated analysis of single-cell RNA-seq and spatial transcriptomics to identify the lactylation-related protein TUBB2A as a potential biomarker for glioblastoma in... by Yifan Xu (87249)

“…</p>Methods<p>We employed functional enrichment algorithms, including AUCell and UCell, to assess lactylation activity in GBM cancer cells. …”