Structural plane recognition effect. by Qingjun Yu (1649473)

“…Based on the PyTorch deep learning framework, the initial U²-Net network weights were set, the learning rate was set to 0.001, the training batch was 4, and the Adam optimizer adaptively adjusted the learning rate during the training process. A dedicated network model for segmenting structural planes was obtained, and the model achieved a maximum F-measure value of 0.749 when the confidence threshold was set to 0.7, with an accuracy rate of up to 0.85 within the range of recall rate greater than 0.5. …”

Structural plane classification. by Qingjun Yu (1649473)

“…Based on the PyTorch deep learning framework, the initial U²-Net network weights were set, the learning rate was set to 0.001, the training batch was 4, and the Adam optimizer adaptively adjusted the learning rate during the training process. A dedicated network model for segmenting structural planes was obtained, and the model achieved a maximum F-measure value of 0.749 when the confidence threshold was set to 0.7, with an accuracy rate of up to 0.85 within the range of recall rate greater than 0.5. …”

Mixup data expansion. by Qingjun Yu (1649473)

“…Based on the PyTorch deep learning framework, the initial U²-Net network weights were set, the learning rate was set to 0.001, the training batch was 4, and the Adam optimizer adaptively adjusted the learning rate during the training process. A dedicated network model for segmenting structural planes was obtained, and the model achieved a maximum F-measure value of 0.749 when the confidence threshold was set to 0.7, with an accuracy rate of up to 0.85 within the range of recall rate greater than 0.5. …”

Marking example. by Qingjun Yu (1649473)

“…Based on the PyTorch deep learning framework, the initial U²-Net network weights were set, the learning rate was set to 0.001, the training batch was 4, and the Adam optimizer adaptively adjusted the learning rate during the training process. A dedicated network model for segmenting structural planes was obtained, and the model achieved a maximum F-measure value of 0.749 when the confidence threshold was set to 0.7, with an accuracy rate of up to 0.85 within the range of recall rate greater than 0.5. …”

Data_Sheet_5_Cell Line-, Protein-, and Sialoglycosite-Specific Control of Flux-Based Sialylation in Human Breast Cells: Implications for Cancer Progression.pdf by Christopher T. Saeui (4246210)

Effect of D₂/D₃ antagonist sulpiride on GBR-induced recognition memory impairments. by Jill Rocchetti (16883936)

Table_3_Alteration of mRNA 5-Methylcytosine Modification in Neurons After OGD/R and Potential Roles in Cell Stress Response and Apoptosis.XLSX by Huan Jian (10077037)

“…Here, we successfully constructed a neuronal oxygen-glucose deprivation/reoxygenation (OGD/R) model and obtained an overview of the transcriptome-wide m⁵C profiles using RNA-BS-seq. …”

Table_1_Alteration of mRNA 5-Methylcytosine Modification in Neurons After OGD/R and Potential Roles in Cell Stress Response and Apoptosis.XLSX by Huan Jian (10077037)

“…Here, we successfully constructed a neuronal oxygen-glucose deprivation/reoxygenation (OGD/R) model and obtained an overview of the transcriptome-wide m⁵C profiles using RNA-BS-seq. …”

Table_2_Alteration of mRNA 5-Methylcytosine Modification in Neurons After OGD/R and Potential Roles in Cell Stress Response and Apoptosis.XLSX by Huan Jian (10077037)

“…Here, we successfully constructed a neuronal oxygen-glucose deprivation/reoxygenation (OGD/R) model and obtained an overview of the transcriptome-wide m⁵C profiles using RNA-BS-seq. …”

Table_4_Alteration of mRNA 5-Methylcytosine Modification in Neurons After OGD/R and Potential Roles in Cell Stress Response and Apoptosis.XLSX by Huan Jian (10077037)

“…Here, we successfully constructed a neuronal oxygen-glucose deprivation/reoxygenation (OGD/R) model and obtained an overview of the transcriptome-wide m⁵C profiles using RNA-BS-seq. …”

Acute effects of HWHF on (A) DPP-IV enzyme activity <i>in vitro</i>, (B) glucose tolerance, (C) plasma insulin and (D) plasma DPP-IV in high fat fed rats. Parameters were assessed... by Prawej Ansari (12191266)

Image5_The overexpression of R-spondin 3 affects hair morphogenesis and hair development along with the formation and maturation of the hair follicle stem cells.TIF by Alicja Olczak (19676284)

DataSheet1_Divalent Metal Uptake and the Role of ZIP8 in Host Defense Against Pathogens.PDF by Derrick R. Samuelson (669543)

Image_5_Genetic deletion of zinc transporter ZnT3 induces progressive cognitive deficits in mice by impairing dendritic spine plasticity and glucose metabolism.jpeg by Rui Zong (7360589)

Cation Disorder and Large Tetragonal Supercell Ordering in the Li-Rich Argyrodite Li₇Zn_0.5SiS₆ by Bernhard T. Leube (5811647)

“…Simultaneous occupation of four types of Li site (T5, T5a, T2, T4) at high temperature and five types of Li site (T5, T2, T4, T1, and a new trigonal planar T2a position) at room temperature is observed. …”

Cation Disorder and Large Tetragonal Supercell Ordering in the Li-Rich Argyrodite Li₇Zn_0.5SiS₆ by Bernhard T. Leube (5811647)

“…Simultaneous occupation of four types of Li site (T5, T5a, T2, T4) at high temperature and five types of Li site (T5, T2, T4, T1, and a new trigonal planar T2a position) at room temperature is observed. …”

Cation Disorder and Large Tetragonal Supercell Ordering in the Li-Rich Argyrodite Li₇Zn_0.5SiS₆ by Bernhard T. Leube (5811647)

“…Simultaneous occupation of four types of Li site (T5, T5a, T2, T4) at high temperature and five types of Li site (T5, T2, T4, T1, and a new trigonal planar T2a position) at room temperature is observed. …”

Atg14 deficiency in Tregs induces a type-I-dominant lymphoproliferative disease and cell-intrinsic reductions in eTregs. by Erienne G. Norton (9612079)

The N-terminal domain is sufficient for CMPK2 antiviral function. by Joanna B. Pawlak (5210639)

Table 5_CX3CR1 upregulation modulates microglial activation and preserves synapses in the hippocampus and frontal cortex of middle-aged mice.xlsx by Jinfeng Liu (32678)