Bone loss in the mouse femoral head is associated with weightlessness in microgravity. by Rukmani Cahill (20939813)

The timeline of the experiment. by Shadi Nazarizadeh (20108029)

“…<div><p>Hypoxia-Induced Neonatal Seizure (HINS) is a prevalent type of seizure in infants caused by hypoxic conditions, which can lead to an increased risk of epilepsy, learning disabilities, and cognitive impairments later in life. …”

S1 Raw data - by Shadi Nazarizadeh (20108029)

“…<div><p>Hypoxia-Induced Neonatal Seizure (HINS) is a prevalent type of seizure in infants caused by hypoxic conditions, which can lead to an increased risk of epilepsy, learning disabilities, and cognitive impairments later in life. …”

S1 Graphical abstract - by Shadi Nazarizadeh (20108029)

“…<div><p>Hypoxia-Induced Neonatal Seizure (HINS) is a prevalent type of seizure in infants caused by hypoxic conditions, which can lead to an increased risk of epilepsy, learning disabilities, and cognitive impairments later in life. …”

Validation and predictive accuracy of the cerebrovascular model, by Hadi Esfandi (21387211)

“…This observation suggests that the myogenic response is potentially linearly potentiated with increasing WT; however, the decreased constriction ability of muscles in the sloped phase, is proven to be advantageous for the vasculature, as it prevents reduced blood flow in deeper layers at high ABNP values. …”

Quantitative results on WEDU dataset. by Dunlu Lu (19964225)

“…This solves the issues of increased training time and redundant weights caused by the detection neck and auxiliary branch structures in traditional YOLOv9, enabling MAR-YOLOv9 to maintain high performance while reducing the model’s computational complexity and improving detection speed, making it more suitable for real-time detection tasks. …”

Counting results on DRPD dataset. by Dunlu Lu (19964225)

“…This solves the issues of increased training time and redundant weights caused by the detection neck and auxiliary branch structures in traditional YOLOv9, enabling MAR-YOLOv9 to maintain high performance while reducing the model’s computational complexity and improving detection speed, making it more suitable for real-time detection tasks. …”

Quantitative results on RFRB dataset. by Dunlu Lu (19964225)

“…This solves the issues of increased training time and redundant weights caused by the detection neck and auxiliary branch structures in traditional YOLOv9, enabling MAR-YOLOv9 to maintain high performance while reducing the model’s computational complexity and improving detection speed, making it more suitable for real-time detection tasks. …”

Main module structure. by Dunlu Lu (19964225)

“…This solves the issues of increased training time and redundant weights caused by the detection neck and auxiliary branch structures in traditional YOLOv9, enabling MAR-YOLOv9 to maintain high performance while reducing the model’s computational complexity and improving detection speed, making it more suitable for real-time detection tasks. …”

Counting results on MTDC-UAV dataset. by Dunlu Lu (19964225)

“…This solves the issues of increased training time and redundant weights caused by the detection neck and auxiliary branch structures in traditional YOLOv9, enabling MAR-YOLOv9 to maintain high performance while reducing the model’s computational complexity and improving detection speed, making it more suitable for real-time detection tasks. …”

Quantitative results on DRPD dataset. by Dunlu Lu (19964225)

“…This solves the issues of increased training time and redundant weights caused by the detection neck and auxiliary branch structures in traditional YOLOv9, enabling MAR-YOLOv9 to maintain high performance while reducing the model’s computational complexity and improving detection speed, making it more suitable for real-time detection tasks. …”

Architecture of MAR-YOLOv9. by Dunlu Lu (19964225)

“…This solves the issues of increased training time and redundant weights caused by the detection neck and auxiliary branch structures in traditional YOLOv9, enabling MAR-YOLOv9 to maintain high performance while reducing the model’s computational complexity and improving detection speed, making it more suitable for real-time detection tasks. …”

Quantitative results on MTDC-UAV dataset. by Dunlu Lu (19964225)

“…This solves the issues of increased training time and redundant weights caused by the detection neck and auxiliary branch structures in traditional YOLOv9, enabling MAR-YOLOv9 to maintain high performance while reducing the model’s computational complexity and improving detection speed, making it more suitable for real-time detection tasks. …”

Counting results on WEDU dataset. by Dunlu Lu (19964225)

“…This solves the issues of increased training time and redundant weights caused by the detection neck and auxiliary branch structures in traditional YOLOv9, enabling MAR-YOLOv9 to maintain high performance while reducing the model’s computational complexity and improving detection speed, making it more suitable for real-time detection tasks. …”

Example images from four plant datasets. by Dunlu Lu (19964225)

“…This solves the issues of increased training time and redundant weights caused by the detection neck and auxiliary branch structures in traditional YOLOv9, enabling MAR-YOLOv9 to maintain high performance while reducing the model’s computational complexity and improving detection speed, making it more suitable for real-time detection tasks. …”

Counting results on RFRB dataset. by Dunlu Lu (19964225)

“…This solves the issues of increased training time and redundant weights caused by the detection neck and auxiliary branch structures in traditional YOLOv9, enabling MAR-YOLOv9 to maintain high performance while reducing the model’s computational complexity and improving detection speed, making it more suitable for real-time detection tasks. …”

Detection visualization results on WEDU dataset. by Dunlu Lu (19964225)

“…This solves the issues of increased training time and redundant weights caused by the detection neck and auxiliary branch structures in traditional YOLOv9, enabling MAR-YOLOv9 to maintain high performance while reducing the model’s computational complexity and improving detection speed, making it more suitable for real-time detection tasks. …”

List of DEGs from neuronal cell analysis. by Sara Cuvertino (18892069)

“…In contrast with the epigenomic changes, the number of DEGs decrease as differentiation progresses. Our analysis reveals significant enrichment of differentially downregulated genes in areas containing putative enhancer regions with H3K4me1 loss. …”

List of HipSci cell lines used in the study. by Sara Cuvertino (18892069)

“…In contrast with the epigenomic changes, the number of DEGs decrease as differentiation progresses. Our analysis reveals significant enrichment of differentially downregulated genes in areas containing putative enhancer regions with H3K4me1 loss. …”

List of DEGs from iPSC analysis. by Sara Cuvertino (18892069)

“…In contrast with the epigenomic changes, the number of DEGs decrease as differentiation progresses. Our analysis reveals significant enrichment of differentially downregulated genes in areas containing putative enhancer regions with H3K4me1 loss. …”