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. …”

Quadrant chart of energy consumption efficiency. by YanFei Lei (19532983)

“…<div><p>The Guangdong-Hong Kong-Macao Greater Bay Area has attracted attention for its extraordinary pace of economic development and is considered to be leading the way in China’s transformation from a manufacturing to an innovation cluster. …”

Drill image dataset for training part II. 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. …”

U²-Net network structure diagram [8]. 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. …”

Environmental regulation intensity of each city. by YanFei Lei (19532983)

“…<div><p>The Guangdong-Hong Kong-Macao Greater Bay Area has attracted attention for its extraordinary pace of economic development and is considered to be leading the way in China’s transformation from a manufacturing to an innovation cluster. …”

RSU-7 structure diagram [8]. 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. …”

Drill image dataset for training part I. 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. …”

Fig 2 - by Marc S. Penn (16624541)

A case of probable esomeprazole-induced transient liver injury in a pregnant woman with hyperemesis by B. , Thomas

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Mitochondrial superoxide increases the lifespan of worms with a detrimental level of cytoplasmic superoxide. by Claire E. Schaar (691681)

“…This is in contrast to <i>clk-1;sod-2</i> worms that exhibit a decrease in lifespan when mitochondrial superoxide levels are further increased. …”

Image 1_Ketogenic diet–induced changes in methylation status and neuropeptide signaling: relationships between S-adenosylmethionine (AdoMet), orexin-A, and metabolic health.pdf by Laura Mosca (704904)

“…</p>Results<p>After 8 weeks, participants exhibited significant reductions in body weight, BMI, visceral adipose tissue, total cholesterol, fasting glycemia, triglycerides, and HbA1c (all p < 0.05). Circulating AdoMet (−75.7%) and Orexin-A (−7.2%) levels also decreased significantly. …”

Schematic diagram domains of phage vB_VspM_VS1 tail fiber genes ORF42 (a), ORF61 (b), ORF64 (c) and ORF82 (d) obtained from the SMART (http://smart.embl.de) database. by Xuemei Duan (6901601)

A decrease in average islet area (<i>P</i><0.05) (a) was associated an increase in the islet count (<i>P</i><0.05) (b) preserving the overall islet fractional area (c). by Lindsey E. Nicol (124842)

“…<p>A decrease in average islet area (<i>P</i><0.05) (a) was associated an increase in the islet count (<i>P</i><0.05) (b) preserving the overall islet fractional area (c).…”

Parameters (means values±SEM) representative of both phases of Tc decrease and Tc increase observed at 25 and 12°C in adult and aged mouse lemurs acclimated to a winter-like or a summer-like photoperiod. by Jeremy Terrien (616965)

“…</p><p><i>Parameters abbreviations: D_decr or D_incr = Duration of Tc decrease or Tc increase; T_decr or T_incr = Amplitude of Tc decrease or Tc increase; A_decr or A_incr = Areas under the Tc curve for Tc decrease or Tc increase.…”

Decrease of the concentration of thymosin β₄ (white symbols) and thymosin β₁₀ (black symbols) in whole saliva as a function of postmenstrual age (days) of a pre-term newborn. by Sonia Nemolato (367239)

“…<p>Decrease of the concentration of thymosin β₄ (white symbols) and thymosin β₁₀ (black symbols) in whole saliva as a function of postmenstrual age (days) of a pre-term newborn.…”

Exponent as a function of capillary diameter. by Jan Karbowski (75084)

Effect of MO-A on BBB disruption. by Mingbao Lin (727751)

DRLE had a cardioprotective effect. by Lung-Shuo Wang (3391244)

Median change score in FA between the first (Pre) and the second (Post) repetition of the SSDT in the CT optimal and non-CT optimal group. by Sofia Sacchetti (9647845)

“…The CT optimal group showed a stronger decrease in FA during the second repetition (Post) of the SSDT as compared to the non-CT optimal group.…”

Fig 1 - by Marc S. Penn (16624541)