Fig 3 - by Alvine M. Akumbom (20460907)

“…The mean difference in GMT for HPV18 between PWH and PWoH was -536.23 (95% CI: -830.66, -241.81); approximately 22 times higher than HPV18 seropositivity cut-offs, assuming milli-Merck Units per milliliter. …”

Summary of characteristics of studies reviewed. by Alvine M. Akumbom (20460907)

“…The mean difference in GMT for HPV18 between PWH and PWoH was -536.23 (95% CI: -830.66, -241.81); approximately 22 times higher than HPV18 seropositivity cut-offs, assuming milli-Merck Units per milliliter. …”

Cumulative meta-analysis. by Alvine M. Akumbom (20460907)

“…The mean difference in GMT for HPV18 between PWH and PWoH was -536.23 (95% CI: -830.66, -241.81); approximately 22 times higher than HPV18 seropositivity cut-offs, assuming milli-Merck Units per milliliter. …”

Summary of immunogenicity information. by Alvine M. Akumbom (20460907)

“…The mean difference in GMT for HPV18 between PWH and PWoH was -536.23 (95% CI: -830.66, -241.81); approximately 22 times higher than HPV18 seropositivity cut-offs, assuming milli-Merck Units per milliliter. …”

<i>In vitro</i> fluorescence imaging and physicochemical characterization of the 3WJ-n × Bro fluorescent aptamer. by Sha Liu (80416)

Fig 9 - BC. by Curtis Chepsiror (21368103)

TraN directs surface exclusion specificity. by Nicolas Rivard (3875746)

Fig 10 - EC. by Curtis Chepsiror (21368103)

Minimal test data set by Wenshun Sheng (21485393)

“…OSA is highly sensitive to information of different scales, and its one-time aggregation property substantially decreases the computational overhead of the model. …”

OSNet network structure. by Wenshun Sheng (21485393)

“…OSA is highly sensitive to information of different scales, and its one-time aggregation property substantially decreases the computational overhead of the model. …”

YOLOv8 overall framework. by Wenshun Sheng (21485393)

“…OSA is highly sensitive to information of different scales, and its one-time aggregation property substantially decreases the computational overhead of the model. …”

The performance of YOFGD model on MOT16. by Wenshun Sheng (21485393)

“…OSA is highly sensitive to information of different scales, and its one-time aggregation property substantially decreases the computational overhead of the model. …”

Network structure of OSA. by Wenshun Sheng (21485393)

“…OSA is highly sensitive to information of different scales, and its one-time aggregation property substantially decreases the computational overhead of the model. …”

The performance of S-YOFEO model on MOT17. by Wenshun Sheng (21485393)

“…OSA is highly sensitive to information of different scales, and its one-time aggregation property substantially decreases the computational overhead of the model. …”

Five multi-target tracking evaluation indexes. by Wenshun Sheng (21485393)

“…OSA is highly sensitive to information of different scales, and its one-time aggregation property substantially decreases the computational overhead of the model. …”

Algorithm flowchart of OFEO. by Wenshun Sheng (21485393)

“…OSA is highly sensitive to information of different scales, and its one-time aggregation property substantially decreases the computational overhead of the model. …”

Partial tracking results of MOT17 dataset. by Wenshun Sheng (21485393)

“…OSA is highly sensitive to information of different scales, and its one-time aggregation property substantially decreases the computational overhead of the model. …”

Improved detection layer. by Wenshun Sheng (21485393)

“…OSA is highly sensitive to information of different scales, and its one-time aggregation property substantially decreases the computational overhead of the model. …”

The performance of S-YOFEO model on MOT16. by Wenshun Sheng (21485393)

“…OSA is highly sensitive to information of different scales, and its one-time aggregation property substantially decreases the computational overhead of the model. …”

The matching process of EIOU. by Wenshun Sheng (21485393)

“…OSA is highly sensitive to information of different scales, and its one-time aggregation property substantially decreases the computational overhead of the model. …”