Bayesian Spatial Binary Regression for Label Fusion in Structural Neuroimaging by D. Andrew Brown (6584837)

Assessing the effectiveness of a melanopsin-based signal for colour constancy - ICVS Presentation 2019 by Daniel Garside (3367640)

“…<br><br>Additionally, the distinct spectral profile and spatial configuration (providing a secondary mesh across the retina) might provide a signal which would allow for point-wise colour constancy transformation, without relying on scene-level properties such as those employed in algorithms based on a ‘grey-world’ assumption.<br><br>To explore this problem, spectral reflectance data of natural objects from the Vrhel+ dataset (Vrhel, Gershon, and Iwan 1994), spectral power distributions from the Hernández-Andrés+ dataset (Hernández-Andrés et al. 2001), and the CIE 2006 10-deg cone fundamentals (CIE 2006), were used to compute chromaticity co-ordinates for a large number of feasible natural colour signals in MB space. …”

Image 1_Construction of a diagnostic model and identification of effect genes for diabetic kidney disease with concurrent vascular calcification based on bioinformatics and multipl... by Lili Huang (125493)

Changes in GABA neurons (RiboTag::Gad2-Cre). by Lech Kaczmarczyk (587512)

Changes in glutamatergic neurons (RiboTag::vGluT2-Cre). by Lech Kaczmarczyk (587512)

Changes in astrocytes (RiboTag::Cx43-Cre). by Lech Kaczmarczyk (587512)

DataSheet_1_Integrated analysis of potential gene crosstalk between non-alcoholic fatty liver disease and diabetic nephropathy.docx by Qianqian Yan (4479328)

“…Ten optimal crosstalk genes were selected by LASSO regression and Boruta algorithm, including CD36, WIPI1, CBX7, FCN1, SLC35D2, CP, ZDHHC3, PTPN3, LPL, and SPP1. …”

Data Sheet 1_Immunogenic cell death-related genes as prognostic biomarkers and therapeutic insights in uterine corpus endometrial carcinoma: an integrative bioinformatics analysis.... by Tianfei Yi (10971822)

“…Differentially expressed genes (DEGs) were identified from transcriptomic data processed with the "DESeq2" R package. …”

Table2_Comprehensive analysis of key m5C modification-related genes in type 2 diabetes.XLSX by Yaxian Song (10454804)

“…</p><p>Methods: The merged gene expression profiles from two Gene Expression Omnibus (GEO) datasets were used to identify m5C-related genes and T2D-related differentially expressed genes (DEGs). Least-absolute shrinkage and selection operator (LASSO) regression analysis was performed to identify optimal predictors of T2D. …”

Table1_Comprehensive analysis of key m5C modification-related genes in type 2 diabetes.XLSX by Yaxian Song (10454804)

“…</p><p>Methods: The merged gene expression profiles from two Gene Expression Omnibus (GEO) datasets were used to identify m5C-related genes and T2D-related differentially expressed genes (DEGs). Least-absolute shrinkage and selection operator (LASSO) regression analysis was performed to identify optimal predictors of T2D. …”

Presentation1_Comprehensive analysis of key m5C modification-related genes in type 2 diabetes.PDF by Yaxian Song (10454804)

“…</p><p>Methods: The merged gene expression profiles from two Gene Expression Omnibus (GEO) datasets were used to identify m5C-related genes and T2D-related differentially expressed genes (DEGs). Least-absolute shrinkage and selection operator (LASSO) regression analysis was performed to identify optimal predictors of T2D. …”

Image1_Comprehensive analysis of key m5C modification-related genes in type 2 diabetes.PDF by Yaxian Song (10454804)

“…</p><p>Methods: The merged gene expression profiles from two Gene Expression Omnibus (GEO) datasets were used to identify m5C-related genes and T2D-related differentially expressed genes (DEGs). Least-absolute shrinkage and selection operator (LASSO) regression analysis was performed to identify optimal predictors of T2D. …”

<b>A Single-cell Transcriptomic Sequencing Dataset of Early Female and Male Chicken (</b><b><i>Gallus gallus</i></b><b>) Embryos</b> by Zhi Cao (20682218)

“…The UMAP algorithm was optimized with a neighborhood size of 20 to achieve optimal cell clustering and clear visual representation of the cell populations.…”